Gemini for Data Scientists and Analysts
Earned mag 3, 2024 EDT
Gemini for Security Engineers
Earned mag 2, 2024 EDT
Gemini for DevOps Engineers
Earned apr 20, 2024 EDT
Gemini for Network Engineers
Earned apr 19, 2024 EDT
Gemini for Application Developers
Earned apr 18, 2024 EDT
Gemini for Cloud Architects
Earned apr 15, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned apr 13, 2024 EDT
Modernizing Mainframe Applications with Google Cloud
Earned apr 8, 2024 EDT
Search with AI Applications
Earned mar 25, 2024 EDT
Vector Search and Embeddings
Earned mar 18, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned feb 24, 2024 EST
Building Resilient Streaming Systems on Google Cloud Platform
Earned feb 17, 2024 EST
Smart Analytics, Machine Learning, and AI on Google Cloud - Italiano
Earned feb 17, 2024 EST
Creazione di pipeline di dati in batch su Google Cloud
Earned gen 31, 2024 EST
Modernizzazione di data lake e data warehouse con Google Cloud
Earned gen 22, 2024 EST
Preparing for your Professional Data Engineer Journey
Earned gen 11, 2024 EST
Conversational AI on Vertex AI and Dialogflow CX
Earned dic 9, 2023 EST
Introduzione a Google Kubernetes Engine
Earned nov 26, 2023 EST
Infrastruttura Google Cloud elastica: scalabilità e automazione
Earned nov 24, 2023 EST
Preparing for the Professional Cloud Architect Exam - Italiano
Earned nov 14, 2023 EST
ML Pipelines on Google Cloud
Earned ott 24, 2023 EDT
Machine Learning Operations (MLOps): Getting Started
Earned ott 18, 2023 EDT
Recommendation Systems on Google Cloud
Earned ott 17, 2023 EDT
Natural Language Processing on Google Cloud
Earned ott 11, 2023 EDT
Computer Vision Fundamentals with Google Cloud
Earned ott 7, 2023 EDT
Production Machine Learning Systems
Earned ott 1, 2023 EDT
Machine learning in azienda
Earned set 28, 2023 EDT
Feature engineering
Earned set 19, 2023 EDT
Crea, addestra ed esegui il deployment di modelli ML tramite Keras su Google Cloud
Earned set 16, 2023 EDT
Launching into Machine Learning - Italiano
Earned set 14, 2023 EDT
How Google Does Machine Learning
Earned set 8, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals - Italiano
Earned set 5, 2023 EDT
Introduzione all'AI e al machine learning su Google Cloud
Earned set 1, 2023 EDT
Generative AI for Business Leaders
Earned ago 28, 2023 EDT
Text Prompt Engineering Techniques
Earned ago 26, 2023 EDT
Implementing Generative AI with Vertex AI
Earned ago 24, 2023 EDT
Introduction to Generative AI Studio - Italiano
Earned ago 23, 2023 EDT
Create Image Captioning Models - Italiano
Earned ago 22, 2023 EDT
Transformer Models and BERT Model - Italiano
Earned ago 22, 2023 EDT
Encoder-Decoder Architecture - Italiano
Earned ago 22, 2023 EDT
Attention Mechanism - Italiano
Earned ago 21, 2023 EDT
Generative AI Explorer : Vertex AI
Earned lug 30, 2023 EDT
Introduction to Image Generation - Italiano
Earned lug 29, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud - Italiano
Earned lug 29, 2023 EDT
Generative AI Fundamentals
Earned lug 28, 2023 EDT
Introduction to Responsible AI - Italiano
Earned lug 28, 2023 EDT
Introduction to Large Language Models - Italiano
Earned lug 28, 2023 EDT
Introduction to Generative AI - Italiano
Earned lug 27, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads - Italiano
Earned ott 14, 2021 EDT
Progettazione dell'architettura con Google Kubernetes Engine: fondamenti
Earned ott 5, 2021 EDT
Infrastruttura Google Cloud affidabile: progetto e processo
Earned set 22, 2021 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation - Locales
Earned set 20, 2021 EDT
This course educates partners on key concepts of Google’s Migrate to Containers. It will cover planning, workload fitness for conversion, deployment with a processing cluster, and the migration process.
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Perform a migration from Oracle to BigQuery using SQL Translation and DataFlow using Sample Data. Learners will complete a quiz that focuses on the process of transferring both schema and data from an Oracle enterprise data warehouse to BigQuery.
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Migration from Oracle to Cloud Spanner using HarbourBridge. This course describes an example scenario that uses sample data during the migration. This process includes using HarbourBridge for Assessment, Schema Conversion, Schema Transformation, Data Migration, and supporting tools for data validation.
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Planning for a Google Workspace Deployment is the final course in the Google Workspace Administration series.
In this course, you will be introduced to Google's deployment methodology and best practices. You will follow Katelyn and Marcus as they plan for a Google Workspace deployment at Cymbal. They'll focus on the core technical project areas of provisioning, mail flow, data migration, and coexistence, and will consider the best deployment strategy for each area.
You will also be introduced to the importance of Change Management in a Google Workspace deployment, ensuring that users make a smooth transition to Google Workspace and gain the benefits of work transformation through communications, support, and training.
This course covers theoretical topics, and does not have any hands on exercises. If you haven’t already done so, please cancel your Google Workspace trial now to avoid any unwanted charges.
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The goals at the end of this course are to be able to articulate to customers when and why they should use Looker’s multistage development framework and to share high-level ways to promote LookML code and content across multiple Looker instances.
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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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The fastest way to improve machine learning outcomes is to focus on your data. In this course you'll review the common challenges with data in ML, and then learn how to overcome these challenges using Vertex AI Feature Stores.
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Cloud technology on its own only provides a fraction of the true value to a business; When combined with data–lots and lots of it–it has the power to truly unlock value and create new experiences for customers.
In this course, you'll learn what data is, historical ways companies have used it to make decisions, and why it is so critical for machine learning. This course also introduces learners to technical concepts such as structured and unstructured data. database, data warehouse, and data lakes. It then covers the most common and fastest growing Google Cloud products around data.
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What is cloud technology or data science? More importantly, what can it do for you, your team, and your business?
If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you.
This course defines foundational terms such as cloud, data, and digital transformation. It also explores examples of companies around the world that are using cloud technology to revolutionize their businesses. The course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and aligns them with the Google Cloud solution pillars. But digital transformation isn't just about using new technology. To truly transform, organizations also need to be innovative and scale an innovation mindset across the organization. The course offers best practices to help you achieve this.
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Explore how to use AI to automate document processing tasks, such as classifying documents, extracting data from documents, and summarizing documents. Learn how to use the Document AI Workbench to create custom document extractors and summarizers. Upload documents, define fields, create versions, and call endpoints to get structured data and summaries back. Discover a new service called Document AI Warehouse, which is a fully managed service to search, store, govern, and manage documents and their extracted metadata. You will also learn about how it integrates with other Google Cloud services like Document AI, BigQuery, and Cloud Storage.
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This course focuses on modernizing applications using OpenShift on Google Cloud. Throughout this course, you'll gain the skills necessary to describe and understand OpenShift and successfully re-platform it to Google Cloud.
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Learn about new generative AI features in App Development, including Duet AI for VS Code, Cloud Workstations and Colab Enterprise, as well as application prototyping using natural language prompts in AppSheet.
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Learn about Generative AI, Vectors and Applications, including vector embedding in PostgreSQL, Cloud SQL for PostgreSQL and the pgvector extension. As well as building Generative AI powered apps faster with Duet AI.
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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.
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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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Migration from on-premises VMware to Google Cloud Compute Engine using Migrate to Virtual Machines (v5) using demo VM(s). It provides a proof-of-concept that walks you through the process of replicating a VM to doing test cutover and final cutover of the VM.
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Want to learn more about Google Cloud?
Grow your Google Cloud knowledge, strengthen your skills to win with customers, and scale your Google Cloud business. Find it here in one handy location.
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Want to learn more about Google Cloud?
Grow your Google Cloud knowledge, strengthen your skills to win with customers, and scale your Google Cloud business. Find it here in one handy location.
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Learn about the new skills you'll need to be successful when using generative AI. Google Cloud has used generative AI to help keep you engaged and streamline your learning journey.
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Explore the four pillars of Enterprise Readiness in generative AI: data governance and privacy, security and compliance support, infrastructure reliability and sustainability, and responsible AI. You will also learn how these pillars address concerns about data privacy and security. Learn about customizing foundation models with your data while keeping your data safe using adapter layers, how to keep your AI models safe and compliant when deploying them across the world, and the multiple layers of encryption, rigorous controls, supply chain audits, and ongoing security testing that are built into Google Cloud. You will also learn about security controls such as VPC, customer-managed encryption keys, access transparency, and data residency zones. And explore enterprise controls, certifications, and responsible AI tooling available in Vertex AI to ensure your data remains secure and compliant with global regulations when deploying generative AI models.
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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks for migrating data from AWS Redshift to BigQuery using BigQuery Data Transfer Service, which includes sample mock data. Learners will complete a challenge lab that focuses on the process of transferring both schema and data from a Redshift data warehouse to BigQuery.
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The Database Summit learning path is a curated collection of courses and quests that provide converage of infrastructure, database migration, and SQL operations.
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The Cloud Foundations Customer Onboarding: Best Practices course enables partners to onboard customers on Google Cloud efficiently and in minimum time, by imparting knowledge, IP, and best practices from the Technical Onboarding Center (TOC) team at Global Delivery Center (GDC). The course explores Cloud Identity and organization, users and groups, administrative access, and resource hierarchy. It also examines network configuration, hybrid connectivity, logging and monitoring, and organizational security.
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RHLF is a technique for fine-tuning language models by incorporating human feedback into the training process. This course explores how you can use RHLF to improve the performance of language models on various tasks, such as text summarization and question answering.
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In this course, you will learn about the Apigee Integration solution and its architecture. You will learn how to identify and develop customer opportunities while differentiating Google's offering from other competitors. Also, the course includes a deep dive into the use of Connectors in Apigee Integrations, as well as demos into how the implementation configurations for design, deployment, monitoring and debugging are carried out.
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This course provides an overview of Network Monitoring and Troubleshooting on Google Cloud.
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This course provided technical training in Google Cloud Dataflow, the foundational pillar of Google Cloud's streaming analytics solution. This training is intended for Google Cloud technical experts that are looking to further their understanding of Dataflow to advance sales-related technical evaluations, customer implementations, technical support, and data processing applications. This course explores topics related to Dataflow, including: Apache Beam SDK Google Cloud Dataflow Runner Autoscaling Logic Sources / Sinks Schemas / Dataflow SQL Dynamic Work RebalancingMonitoring, Troubleshooting, and Optimization Testing and CI/CD
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In this course, you will learn about GDC air-gapped (previously known as GDC Hosted), an offering from Google Distributed Cloud. This course provides both a business and technical overview of GDC air-gapped, exploring its key features and target customers.
Participants will gain insights into GDC air-gapped's value proposition and learn how to effectively communicate its benefits to potential clients, enabling them to qualify for sales opportunities.
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As organizations move their data and applications to the cloud, they must address new security challenges. The Trust and Security with Google Cloud course explores the basics of cloud security, the value of Google Cloud's multilayered approach to infrastructure security, and how Google earns and maintains customer trust in the cloud.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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This course further explores SQL Server on Google Cloud.
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Certificate Authority Service is a highly-available, scalable Google Cloud service. This course covers how Certificate Authority Service enables IT and security teams to simplify and automate the deployment, management, and security of private certificate authorities (CA) while staying in control of their private keys.
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This course focuses on how you can leverage the Google Cloud Analytics and AI/ML offerings to integrate and innovate with SAP
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This course gives you a deep dive into the workflows of Tier 3 analysts.
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In this course you will discover additional tools for your toolbox for working with complex deployments, building robust solutions, and delivering even more value.
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Hands on course covering the main uses of extends and the three primary LookML objects extends are used on as well as some advanced usage of extends.
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This course reviews the processes for creating table calculations, pivots and visualizations
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This course has been updated, please enroll in the new
Elastic Google Cloud Infrastructure: Scaling and Automation.
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The first course provides a high-level overview of security fundamentals on the GDC platform.
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The course explores advanced services such as machine learning, and operational topics such as application deployment, monitoring, and troubleshooting.
In addition, we’ll introduce GDC software upgrades, logging, billing, and cost monitoring.
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The course examines service resources or workload components that exist in projects. You’ll learn about Kubernetes in GDC, Artifact Registry, GDC Object Storage, Database Service, Networking, and Key Management and Security.
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This L300 course explores the intricacies of the hardware and networking infrastructure, examines the role of Kubernetes in container orchestration, and how to master the deployment process. The course emphasizes critical security aspects, guiding you through defense-in-depth design, zero-trust architecture, and essential operational security measures for protecting sensitive data. You'll also gain valuable insights into operational aspects, such as resource management, upgrades, and solutions tailored for GDC customers.
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This course provides an introduction to the GDC platform—which enables you to host, control, and manage infrastructure and services directly on your premises.
GDC air-gapped is one component of Google Distributed Cloud offering which aligns to Google’s digital sovereignty vision. It supports public-sector customers and commercial entities that have strict data residency, security or privacy requirements.
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This L200 course comprehensively explores GDC air-gapped's concepts, architecture, and operational aspects, equipping learners with the knowledge to deploy and manage this solution effectively. The course delves into topics such as the roles of vendors and partners, hardware and software components, zero trust security, multi-tenancy, support and operations, observability, Identity and Access Management, managed services, and the GDC Sandbox environment. Furthermore, the course provides insights into compliance and accreditation processes, ensuring learners understand the regulatory landscape and can navigate it successfully. By the end of this course, learners will have a solid understanding of GDC air-gapped and be prepared to leverage its capabilities for their organization's needs.
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This L200 course comprehensively explores GDC connected concepts, architecture, and operational aspects, equipping learners with the knowledge to deploy and manage this solution effectively. The course delves into topics such as its survivability features and best practices, security, networking, software stack, and hardware options. Furthermore, the course provides insights into its operating model. By the end of this course, learners will have a solid understanding of GDC connected and be prepared to leverage its capabilities for their organization's needs.
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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. “Innovating with Google Cloud Artificial Intelligence” explores how organizations can use AI and ML to transform their business processes.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Explore Generative AI in API management and Application Integration, including Duet AI in Apigee, and extensions for Vertex AI. Discover the new opportunities with generative AI, including conversational APIs, Auto-Operators, and API growth. Use Duet AI to create an API specification in-context, and use Duet AI to create an integration. Use Duet AI in Apigee API Hub, and create an LLM extension.
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Gemini per Google Workspace è un componente aggiuntivo che fornisce ai clienti funzionalità di AI generativa in Google Workspace. In questo mini corso imparerai le funzionalità principali di Gemini e come possono essere utilizzate per migliorare la produttività e l'efficienza in Presentazioni Google.
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Discover how to use Colab Enterprise, a managed notebook environment that provides secure and compliant storage for your notebooks, that comes with two code-generation features: code complete and code gen. Create and use runtime templates in Vertex AI Workbench to give users access to more powerful compute resources while still maintaining control over the types of resources that are spun up. Share notebooks with other users and use versioning to keep track of changes to your notebooks. Learn how Colab Enterprise integrates BigQuery and Vertex AI. You will see how to pull data from BigQuery, use BQML to train a model, and have it all integrated with Vertex Model Registry. Explore how to fine-tune a Foundation model or generative AI model using the Vertex AI SDK. And, learn how to evaluate a tuned model and compare the results of multiple runs.
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DORA (DevOps Research & Assessment) is a research program, an assessment tool, a report publisher, and more. Together, these products create a compelling customer story that defines the industry standard for successful DevOps and technology transformation, and provides personalized steps to accelerate the customer journey. DORA enables Googlers and Partners to bring DevOps research and practices to Google Cloud Customers. This course provides an introduction to DORA and a guide on how to successfully complete a DORA assessment for your customer. Engaging customers in DORA assessment provides invaluable insights into the customer’s organization, and helps you better support your customer. The DORA training was originally designed for and only made available to Google Teams, however we’ve recognized how beneficial it would be for our Partners and are now offering our Partners exclusive access to the DORA training and products, so they can benefit from DORA’s research and practices …
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Not all ML workloads benefit from hardware acceleration, but when they do, Google Cloud has you covered. Learn when and how to use GPU and TPU accelerators most effectively in your ML workloads on Google Cloud.
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Model experimentation and evaluation are critical steps in the journey to productionalize an LLM. This course introduces new tools that will help simplify these tasks.
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An introduction to Large models, Cloud TPUs and GKE. 15 step training for how to get started with Cloud TPUs and GKE, and explore training jobs, example workloads and inference with TPUs on GKE. Discover an app using personalized generative AI.
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Google Cloud : Prompt Engineering Guide examines generative AI tools, how they work. We'll explore how to combine Google Cloud knowledge with prompt engineering to improve Gemini responses.
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Learn how to use NotebookLM to create a personalized study guide for the Professional Machine Learning Engineer certification exam (PMLE). You'll review NotebookLM features, create a notebook, and use the study guide to practice for a certification exam.
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Artificial Intelligence (AI) offers transformative possibilities, but it also introduces new security challenges. This course equips security and data protection leaders with strategies to securely manage AI within their organizations. Learn a framework for proactively identifying and mitigating AI-specific risks, protecting sensitive data, ensuring compliance, and building a resilient AI infrastructure. Pick use cases from four different industries to explore how these strategies apply in real-world scenarios.
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This course introduces you to the world of reliable deep learning, a critical discipline focused on developing machine learning models that not only make accurate predictions but also understand and communicate their own uncertainty. You'll learn how to create AI systems that are trustworthy, robust, and adaptable, particularly in high-stakes scenarios where errors can have significant consequences.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.
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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
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Welcome to the third course of the "Networking in Google Cloud" series: Network Architecture!
In this course, you will explore the fundamentals of designing efficient and scalable network architectures within Google Cloud.
In the first module, Introduction to Network Architecture, we'll start by introducing you to the core components and concepts of network architecture, including subnets, routes, firewalls, and load balancing.
Then in the second module, network topologies, we'll dive into various network topologies commonly used in Google Cloud, discussing their strengths, and weaknesses.
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Welcome to the second course in the networking and Google Cloud series routing and addressing.
In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities.
Module one will lay the foundation by exploring network routing and addressing in Google Cloud, covering key building blocks such as routing IPv4, bringing your own IP addresses and setting up cloud DNS. In Module two will shift our focus to private connection options, exploring use cases and methods for accessing Google and other services privately using internal IP addresses.
By the end of this course, you'll have a solid grasp of how to effectively route and address your network traffic within Google Cloud.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.
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Il corso Google Cloud Computing Foundations fornirà a chi ha poca o nessuna esperienza
di cloud computing una panoramica dettagliata dei concetti relativi alle nozioni di base
del cloud, ai big data e al machine learning, oltre che a dove e come Google Cloud si inserisce.
Alla fine del corso, i partecipanti saranno in grado di descrivere i concetti relativi al cloud computing,
ai big data e al machine learning e dimostrare delle competenze pratiche.
Questo corso fa parte della serie di corsi Google Cloud Computing Foundations.
I corsi dovrebbero essere completati nel seguente ordine:
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales
Questo secondo corso esamina l'implementazione di modelli …
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In this beginner-level course, you will learn about the Data Analytics workflow on Google Cloud and the tools you can use to explore, analyze, and visualize data and share your findings with stakeholders. Using a case study along with hands-on labs, lectures, and quizzes/demos, the course will demonstrate how to go from raw datasets to clean data to impactful visualizations and dashboards. Whether you already work with data and want to learn how to be successful on Google Cloud, or you’re looking to progress in your career, this course will help you get started. Almost anyone who performs or uses data analysis in their work can benefit from this course.
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Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities.
This course teaches you to use Generative AI App Builder to integrate enterprise-grade generative AI search.
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Get hands-on experience applying and building rules for Chronicle. You learn what YARA-L is and how to customize & create event rules.
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This course helps you understand how to use Chronicle to properly handle security incidents.
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Learn the technical aspects you need to know about Chronicle and how it can help you detect and action threats.
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This course helps developers customize Chronicle and augment its abilities with third party integrations.
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This course is for Partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases.
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This course is for Partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities, learn to identify high-impact use cases, and develop the skills to demonstrate and integrate these technologies seamlessly into client solutions and operations.
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This course is for Google Cloud’s top partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases. Those who complete the training and assessment will receive the Google Cloud Generative AI Trailblazer badge through Skills Boost.
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Take the next steps in working with the Chronicle Security Operations Platform. Build on fundamental knowledge to go deeper on cusotmization and tuning.
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This course covers the baseline skills needed for the Google Security Operations Platform. The modules will cover specific actions and features that security engineers should become familiar with to start using the toolset.
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This course will familiarize you with the core functionality of Chronicle, including the user interface, connections, and settings.
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Learn which Mandiant products directly enhance or augment capabilities provided by Chronicle SIEM and SOAR and how those products integrate into our workflow.
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This course will provide you with an overview of SIEM technology to set the stage for the differentiation and expansion of capabilities that Chronicle SIEM provides.
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Il corso Google Cloud Computing Foundations fornirà a chi ha poca o nessuna esperienza
di cloud computing una panoramica dettagliata dei concetti relativi alle nozioni di base
del cloud, ai big data e al machine learning, oltre che a dove e come Google Cloud si inserisce.
Alla fine del corso, i partecipanti saranno in grado di descrivere questi concetti e dimostrare delle competenze pratiche.
Questo corso fa parte della serie di corsi Google Cloud Computing Foundations.
I corsi dovrebbero essere completati nel seguente ordine:
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales
Questo primo corso fornisce una panoramica del cloud computing, dei modi per utilizzare Google Cloud e
diverse opzio…
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Outline the key steps in publishing an API to deliver selective company information to applications created by external developers.
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Non è un segreto che il machine learning sia uno dei campi in più rapida crescita nel settore tecnologico e la piattaforma Google Cloud è stata fondamentale per promuoverne lo sviluppo. Con le numerose API, Google Cloud dispone di uno strumento adeguato praticamente per qualsiasi job di machine learning. In questo corso introduttivo, farai pratica con il machine learning applicato all'elaborazione del linguaggio naturale partecipando ai lab che ti consentiranno di estrarre entità da un testo ed eseguire analisi del sentiment e della sintassi, nonché utilizzare l'API Speech-to-Text per la trascrizione.
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This course provides comprehensive skills on VM migration, from the initial assessment through the final implementation through presentations, demonstrations, and whiteboard session.
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Moving to the cloud creates numerous opportunities to start working in
a new way and it empowers the workforce to better collaborate and innovate. But
it’s also a big change. Sometimes the success of the change hinges not on the change
itself, but on how it’s managed.
This course will help people managers to understand
some of the key challenges associated with cloud adoption, and provide them with
a verified in-the-field framework that will assist them in supporting their teams
on the change journey. By addressing the human factor of moving to the cloud, organizations
increase their chances of realizing business objectives and investing in their future
talent.
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The Google Cloud Rapid Migration & Modernization Program (RaMP) is a holistic, end-to-end migration/modernization program that helps customers & partners leverage expertise and best practices, lower risk, control costs, and simplify a customer's path to cloud success. This course will give an overview of the program and some of the tools and best practices available to support customer migrations & modernizations.
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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Identity, Resource Manager, IAM, Virtual Private Cloud firewalls, Cloud Load Balancing, Cloud Peering, Cloud Interconnect, and VPC Service Controls.
This is the first course of the Security in Google Cloud series. After completing this course, enroll in the Security Best Practices in Google Cloud course.
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This course helps learners prepare for the Professional Cloud Security Engineer (PCSE) Certification exam. Learners will be exposed to and engage with exam topics through a series of lectures, diagnostic questions, and knowledge checks. After completing this course, learners will have a personalized workbook that will guide them through the rest of their certification readiness journey.
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Welcome to Hybrid Cloud Infrastructure Foundations with Anthos!
This is the first course of the Architecting Hybrid Cloud Infrastructure with Anthos path. Anthos enables you to build and manage modern applications, and gives you the freedom to choose where to run them. Anthos gives you one consistent experience in both your on-premises and cloud environments.
During this course, you will be presented with modules that will take you through skills that you will use as an architect or administrator running Anthos environments. The modules in this course include videos, hands-on labs, and links to helpful documentation.
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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search.
You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.
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This course explores the different products and capabilities of Gemini Enterprise for Customer Experience and Conversational Agents. Additionally, it covers the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel.
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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you use Google products and services to develop, test, deploy, and manage applications. With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data. Using a hands-on lab, you experience how Gemini improves the software development lifecycle (SDLC).
Duet AI was renamed to Gemini, our next-generation model.
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Learn how Gemini can revolutionize your ability to develop applications! This course helps developers go beyond the basics and learn how to integrate Gemini into their workflows.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows.
Duet AI was renamed to Gemini, our next-generation model.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you secure your cloud environment and resources. You learn how to deploy example workloads into an environment in Google Cloud, identify security misconfigurations with Gemini, and remediate security misconfigurations with Gemini. Using a hands-on lab, you experience how Gemini improves your cloud security posture.
Duet AI was renamed to Gemini, our next-generation model.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps engineers manage infrastructure. You learn how to prompt Gemini to find and understand application logs, create a GKE cluster, and investigate how to create a build environment. Using a hands-on lab, you experience how Gemini improves the DevOps workflow.
Duet AI was renamed to Gemini, our next-generation model.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps network engineers create, update, and maintain VPC networks. You learn how to prompt Gemini to provide specific guidance for your networking tasks, beyond what you would receive from a search engine. Using a hands-on lab, you experience how Gemini makes it easier for you to work with Google Cloud VPC networks.
Duet AI was renamed to Gemini, our next-generation model.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps developers build applications. You learn how to prompt Gemini to explain code, recommend Google Cloud services, and generate code for your applications. Using a hands-on lab, you experience how Gemini improves the application development workflow.
Duet AI was renamed to Gemini, our next-generation model.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps administrators provision infrastructure. You learn how to prompt Gemini to explain infrastructure, deploy GKE clusters and update existing infrastructure. Using a hands-on lab, you experience how Gemini improves the GKE deployment workflow.
Duet AI was renamed to Gemini, our next-generation model.
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(This course was previously named Multimodal Prompt Engineering with Gemini and PaLM) This course teaches how to use Vertex AI Studio, a Google Cloud console tool for rapidly prototyping and testing generative AI models. You learn to test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application's needs. Whether you are looking for text, chat, code, image or speech generative experiences Vertex AI Studio offers you an interface to work with and APIs to integrate your production application.
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This course enables system integrators and partners to understand the principles of automated migrations, plan legacy system migrations to Google Cloud leveraging G4 Platform, and execute a trial code conversion.
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(Previously named "Developing apps with Vertex AI Agent Builder: Search". Please note there maybe instances in this course where previous product names and titles are used)
Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities.
This course teaches you to use AI Applications to integrate enterprise-grade generative AI search.
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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine.
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.
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L'integrazione del machine learning nelle pipeline di dati aumenta la capacità di estrarre insight dai dati. Questo corso illustra i modi in cui il machine learning può essere incluso nelle pipeline di dati su Google Cloud. Per una personalizzazione minima o nulla, il corso tratta di AutoML. Per funzionalità di machine learning più personalizzate, il corso introduce Notebooks e BigQuery Machine Learning (BigQuery ML). Inoltre, il corso spiega come mettere in produzione soluzioni di machine learning utilizzando Vertex AI.
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Le pipeline di dati in genere rientrano in uno dei paradigmi EL (Extract, Load), ELT (Extract, Load, Transform) o ETL (Extract, Transform, Load). Questo corso descrive quale paradigma dovrebbe essere utilizzato e quando per i dati in batch. Inoltre, questo corso tratta diverse tecnologie su Google Cloud per la trasformazione dei dati, tra cui BigQuery, l'esecuzione di Spark su Dataproc, i grafici della pipeline in Cloud Data Fusion e trattamento dati serverless con Dataflow. Gli studenti fanno esperienza pratica nella creazione di componenti della pipeline di dati su Google Cloud utilizzando Qwiklabs.
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I due componenti chiave di qualsiasi pipeline di dati sono costituiti dai data lake e dai data warehouse. In questo corso evidenzieremo i casi d'uso per ogni tipo di spazio di archiviazione e approfondiremo i dettagli tecnici delle soluzioni di data lake e data warehouse disponibili su Google Cloud. Inoltre, descriveremo il ruolo di un data engineer, illustreremo i vantaggi di una pipeline di dati di successo per le operazioni aziendali ed esamineremo i motivi per cui il data engineering dovrebbe essere eseguito in un ambiente cloud.
Questo è il primo corso della serie Data engineering su Google Cloud. Dopo il completamento di questo corso, iscriviti al corso Creazione di pipeline di dati in batch su Google Cloud.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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In this course you will learn how to use the new generative AI features in Dialogflow CX to create virtual agents that can have more natural and engaging conversations with customers. Discover how to deploy generative fallback responses to gracefully handle errors and omissions in customer conversations, deploy generators to increase intent coverage, and structure, ingest, and manage data in a data store. And explore how to deploy and maintain generative AI agents using your data, and deploy and maintain hybrid agents in combination with existing intent-based design paradigms.
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Ti diamo il benvenuto nel corso Introduzione a Google Kubernetes Engine. Se ti interessa Kubernetes, un livello software che si trova tra le tue applicazioni e la tua infrastruttura hardware, allora sei nel posto giusto. Google Kubernetes Engine ti offre Kubernetes come servizio gestito su Google Cloud.
L'obiettivo di questo corso è illustrare le nozioni di base di Google Kubernetes Engine, o GKE, come viene comunemente chiamato, e come containerizzare le applicazioni e farle funzionare su Google Cloud. Il corso inizia con un'introduzione di base a Google Cloud, seguita da una panoramica dei container e di Kubernetes, dell'architettura di Kubernetes e delle operazioni di Kubernetes.
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Questo corso accelerato on demand illustra ai partecipanti l'infrastruttura completa e flessibile e i servizi di piattaforma forniti da Google Cloud. Attraverso una combinazione di videolezioni, demo e lab pratici, i partecipanti potranno esplorare gli elementi delle soluzioni, tra cui interconnessione sicura delle reti, bilanciamento del carico, scalabilità automatica, automazione dell'infrastruttura e servizi gestiti.
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Lo scopo di questo corso è aiutare coloro che sono qualificati ad avere confidenza per tentare l'esame e aiutare le persone non ancora qualificate a sviluppare il proprio piano per la preparazione.
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In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models.
The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.
The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models.
Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.
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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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Questo corso adotta un approccio pratico reale al flusso di lavoro ML attraverso un case study. Un team ML è chiamato a rispondere a numerosi requisiti aziendali e ad affrontare vari casi d'uso ML. Deve comprendere gli strumenti necessari per la gestione e la governance dei dati e considerare l'approccio migliore per la pre-elaborazione dei dati.
Al team vengono presentate tre opzioni per creare modelli ML per due casi d'uso. Il corso spiega perché il team utilizzerà AutoML, BigQuery ML o l'addestramento personalizzato per raggiungere i propri obiettivi.
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Questo corso illustra i vantaggi dell'utilizzo di Vertex AI Feature Store, come migliorare l'accuratezza dei modelli di ML e come trovare le colonne di dati che forniscono le caratteristiche più utili. Il corso include inoltre contenuti e lab sul feature engineering utilizzando BigQuery ML, Keras e TensorFlow.
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Questo corso tratta la creazione di modelli ML con TensorFlow e Keras, il miglioramento dell'accuratezza dei modelli ML e la scrittura di modelli ML per l'uso su larga scala.
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Il corso inizia con una discussione sui dati: come migliorare la qualità dei dati ed eseguire analisi esplorative dei dati. Descriveremo Vertex AI AutoML e come creare, addestrare ed eseguire il deployment di un modello di ML senza scrivere una sola riga di codice. Comprenderai i vantaggi di Big Query ML. Discuteremo quindi di come ottimizzare un modello di machine learning (ML) e di come la generalizzazione e il campionamento possano aiutare a valutare la qualità dei modelli di ML per l'addestramento personalizzato.
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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Questo corso presenta i prodotti e i servizi per big data e di machine learning di Google Cloud che supportano il ciclo di vita dai dati all'IA. Esplora i processi, le sfide e i vantaggi della creazione di una pipeline di big data e di modelli di machine learning con Vertex AI su Google Cloud.
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Questo corso presenta le offerte di intelligenza artificiale (AI) e machine learning (ML) su Google Cloud per la creazione di progetti di AI predittiva e generativa. Esplora le tecnologie, i prodotti e gli strumenti disponibili durante tutto il ciclo di vita data-to-AI, includendo le basi, lo sviluppo e le soluzioni di AI. Ha lo scopo di aiutare data scientist, sviluppatori di AI e ML engineer a migliorare le proprie abilità e conoscenze attraverso attività di apprendimento coinvolgenti ed esercizi pratici.
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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.
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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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This course will help ML Engineers, Developers, and Data Scientists implement Large Language Models for Generative AI use cases with Vertex AI. The first two modules of this course contain links to videos and prerequisite course materials that will build your knowledge foundation in Generative AI. Please do not skip these modules. The advanced modules in this course assume you have completed these earlier modules.
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Questo corso illustra Generative AI Studio, un prodotto su Vertex AI che ti aiuta a prototipare e personalizzare i modelli di AI generativa in modo da poterne utilizzare le capacità nelle tue applicazioni. In questo corso imparerai cos'è Generative AI Studio, le sue funzionalità e opzioni e come utilizzarlo, esaminando le demo del prodotto. Alla fine, troverai un laboratorio pratico per mettere in pratica ciò che hai imparato e un quiz per testare le tue conoscenze.
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Questo corso ti insegna come creare un modello per le didascalie delle immagini utilizzando il deep learning. Scoprirai i diversi componenti di un modello per le didascalie delle immagini, come l'encoder e il decoder, e imparerai ad addestrare e valutare il tuo modello. Alla fine di questo corso, sarai in grado di creare modelli personali per le didascalie delle immagini e utilizzarli per generare didascalie per le immagini.
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Questo corso ti introduce all'architettura Transformer e al modello BERT (Bidirectional Encoder Representations from Transformers). Scopri i componenti principali dell'architettura Transformer, come il meccanismo di auto-attenzione, e come viene utilizzata per creare il modello BERT. Imparerai anche le diverse attività per le quali può essere utilizzato il modello BERT, come la classificazione del testo, la risposta alle domande e l'inferenza del linguaggio naturale. Si stima che il completamento di questo corso richieda circa 45 minuti.
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Questo corso ti offre un riepilogo dell'architettura encoder-decoder, che è un'architettura di machine learning potente e diffusa per attività da sequenza a sequenza come traduzione automatica, riassunto del testo e risposta alle domande. Apprenderai i componenti principali dell'architettura encoder-decoder e come addestrare e fornire questi modelli. Nella procedura dettagliata del lab corrispondente, implementerai in TensorFlow dall'inizio un semplice codice dell'architettura encoder-decoder per la generazione di poesie da zero.
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Questo corso ti introdurrà al meccanismo di attenzione, una potente tecnica che consente alle reti neurali di concentrarsi su parti specifiche di una sequenza di input. Imparerai come funziona l'attenzione e come può essere utilizzata per migliorare le prestazioni di molte attività di machine learning, come la traduzione automatica, il compendio di testi e la risposta alle domande.
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This content is deprecated. Please see the latest version of the course, here.
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Questo corso introduce i modelli di diffusione, una famiglia di modelli di machine learning che recentemente si sono dimostrati promettenti nello spazio di generazione delle immagini. I modelli di diffusione traggono ispirazione dalla fisica, in particolare dalla termodinamica. Negli ultimi anni, i modelli di diffusione sono diventati popolari sia nella ricerca che nella produzione. I modelli di diffusione sono alla base di molti modelli e strumenti di generazione di immagini all'avanguardia su Google Cloud. Questo corso ti introduce alla teoria alla base dei modelli di diffusione e a come addestrarli ed eseguirne il deployment su Vertex AI.
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Dal momento che l'uso dell'intelligenza artificiale e del machine learning nelle aziende continua a crescere, cresce anche l'importanza di realizzarli in modo responsabile. Molti sono scoraggiati dal fatto che parlare di IA responsabile può essere più facile che metterla in pratica. Se vuoi imparare come operativizzare l'IA responsabile nella tua organizzazione, questo corso fa per te.
In questo corso scoprirai come Google Cloud ci riesce attualmente, oltre alle best practice e alle lezioni apprese, per fungere da framework per costruire il tuo approccio all'IA responsabile.
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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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Questo è un corso di microlearning di livello introduttivo volto a spiegare cos'è l'IA responsabile, perché è importante e in che modo Google implementa l'IA responsabile nei propri prodotti. Introduce anche i 7 principi dell'IA di Google.
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Questo è un corso di microlearning di livello introduttivo che esplora cosa sono i modelli linguistici di grandi dimensioni (LLM), i casi d'uso in cui possono essere utilizzati e come è possibile utilizzare l'ottimizzazione dei prompt per migliorare le prestazioni dei modelli LLM. Descrive inoltre gli strumenti Google per aiutarti a sviluppare le tue app Gen AI.
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Questo è un corso di microlearning di livello introduttivo volto a spiegare cos'è l'AI generativa, come viene utilizzata e in che modo differisce dai tradizionali metodi di machine learning. Descrive inoltre gli strumenti Google che possono aiutarti a sviluppare le tue app Gen AI.
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In questo corso, "Architecting with Google Kubernetes Engine: Workloads", imparerai come eseguire le operazioni Kubernetes, creare e gestire i deployment, quali sono gli strumenti di networking di GKE e come fornire archiviazione permanente ai tuoi carichi di lavoro Kubernetes.
Questo è il secondo corso della serie Architecting with Google Kubernetes Engine. Dopo il completamento di questo corso, iscriviti al corso Reliable Google Cloud Infrastructure: Design and Process o al corso Hybrid Cloud Infrastructure Foundations with Anthos.
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In questo corso, "Progettazione dell'architettura con Google Kubernetes Engine: fondamenti", troverai un ripasso del layout e dei principi di Google Cloud, seguito da un'introduzione alla creazione e alla gestione dei container software, nonché all'architettura di Kubernetes.
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Questo corso spiega agli studenti come creare soluzioni efficienti e ad alta affidabilità su Google Cloud utilizzando pattern di progettazione comprovati. È la continuazione del corso Progettazione dell'architettura con Google Compute Engine o Progettazione dell'architettura con Google Kubernetes Engine e presuppone che si abbia esperienza pratica con le tecnologie esaminate in uno dei due corsi. Attraverso una combinazione di presentazioni, attività di progettazione e lab pratici, i partecipanti impareranno a definire e bilanciare i requisiti aziendali e tecnici per progettare deployment Google Cloud estremamente affidabili, sicuri, economicamente convenienti e ad alta disponibilità.
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This course version is for non-English only. If you wish to take this course in English, please enroll here: Elastic Google Cloud Infrastructure: Scaling and Automation. If you wish to take it in another language, change your language in settings to see availability.
Migrate to Containers using the Migration Assessment Tool
Earned mag 22, 2025 EDT
Oracle to BigQuery Migration
Earned mag 21, 2025 EDT
Oracle to Cloud Spanner
Earned mag 18, 2025 EDT
DEPRECATED Planning for a Google Workspace Deployment
Earned mag 18, 2025 EDT
Inside Track: Looker Multistage Development Framework
Earned mag 10, 2025 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned mag 10, 2025 EDT
High performance feature engineering for predictive and generative AI projects
Earned mag 10, 2025 EDT
Innovating with Data and Google Cloud
Earned mag 3, 2025 EDT
Introduction to Digital Transformation with Google Cloud
Earned mag 3, 2025 EDT
Generative AI for Document Processing
Earned mag 3, 2025 EDT
Modernization of Applications using Openshift on Google Cloud
Earned mag 2, 2025 EDT
New Generative AI features in App Development
Earned mag 2, 2025 EDT
Build Generative AI powered applications
Earned mag 2, 2025 EDT
Data Warehousing for Partners: Enable Google Cloud Customers
Earned apr 29, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned apr 27, 2025 EDT
On-Premises VMware to Compute Engine
Earned apr 25, 2025 EDT
Partner Pre-Sales Readiness Training
Earned apr 25, 2025 EDT
Partner Sales Readiness Training
Earned apr 25, 2025 EDT
Upgrading your skills to work with Generative AI
Earned apr 25, 2025 EDT
Enterprise Readiness in Generative AI
Earned apr 23, 2025 EDT
Redshift to BigQuery
Earned apr 19, 2025 EDT
Database Summit 2022
Earned apr 18, 2025 EDT
Cloud Foundations: Customer Onboarding Best Practices
Earned apr 16, 2025 EDT
Reinforcement Learning with Human Feedback (RLHF)
Earned apr 14, 2025 EDT
Inside Track: Authoring and Connectors in Google Apigee Integration
Earned apr 11, 2025 EDT
Inside Track: Cloud Network Operations: Network Monitoring and Troubleshooting
Earned apr 10, 2025 EDT
Inside Track: Dataflow Advanced
Earned apr 10, 2025 EDT
Introduction to Google Distributed Cloud air-gapped
Earned apr 8, 2025 EDT
Trust and Security with Google Cloud
Earned apr 4, 2025 EDT
Inside Track: SQL Server - Advanced
Earned apr 3, 2025 EDT
Inside Track: Certificate Authority Service
Earned apr 3, 2025 EDT
Analytics for SAP on Google Cloud
Earned apr 3, 2025 EDT
SecOps on GDC for Tier 3 Analysts
Earned mar 28, 2025 EDT
Technology + Beyond the UI
Earned mar 27, 2025 EDT
Extends to Keep LookML DRY
Earned mar 18, 2025 EDT
Table Calculations, Pivots, and Visualizations
Earned mar 10, 2025 EDT
Elastic Cloud Infrastructure: Scaling and Automation
Earned mar 2, 2025 EST
Introduction to SecOps on GDC
Earned feb 15, 2025 EST
AI Services and GDC Deployments and Operations
Earned feb 4, 2025 EST
Compute, Network, and Storage Services Configuration in GDC
Earned feb 2, 2025 EST
L300 Google Distributed Cloud air-gapped
Earned gen 27, 2025 EST
GDC Platform Introduction
Earned gen 3, 2025 EST
L200 Google Distributed Cloud air-gapped
Earned dic 19, 2024 EST
L200 Google Distributed Cloud Connected
Earned dic 14, 2024 EST
Innovating with Google Cloud Artificial Intelligence
Earned dic 12, 2024 EST
Generative AI in App Integration
Earned dic 12, 2024 EST
Gemini in Presentazioni Google
Earned dic 11, 2024 EST
Building AI with Colab Enterprise
Earned dic 10, 2024 EST
Inside Track: DORA
Earned dic 10, 2024 EST
Accelerating Generative AI pipelines with GPUs and TPUs
Earned dic 9, 2024 EST
Experimenting and Evaluating your Gen AI models
Earned dic 8, 2024 EST
Operationalizing large scale machine learning (ML) on Cloud TPUs with Google Kubernetes Engine (GKE)
Earned dic 8, 2024 EST
Google Cloud: Prompt Engineering Guide
Earned dic 7, 2024 EST
Build a Certification Study Guide: PMLE
Earned dic 6, 2024 EST
Introduction to Security in the World of AI
Earned dic 6, 2024 EST
Introduction to Reliable Deep Learning
Earned dic 5, 2024 EST
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned nov 23, 2024 EST
Serverless Data Processing with Dataflow: Operations
Earned nov 15, 2024 EST
Networking in Google Cloud: Network Architecture
Earned nov 12, 2024 EST
Networking in Google Cloud: Routing and Addressing
Earned nov 11, 2024 EST
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Earned ott 28, 2024 EDT
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Earned ott 22, 2024 EDT
Introduction to Data Analytics on Google Cloud
Earned ott 7, 2024 EDT
Enterprise Search on Generative AI App Builder
Earned set 28, 2024 EDT
Google Security Operations - SIEM Rules
Earned set 3, 2024 EDT
Google Security Operations - SOAR Analyst
Earned ago 26, 2024 EDT
Security Practices with Google Security Operations - SIEM
Earned ago 13, 2024 EDT
Google Security Operations - SOAR Developer
Earned ago 12, 2024 EDT
Unlocking the Power of Google Cloud Generative AI for Partners
Earned ago 3, 2024 EDT
Selling the Platform & Building Client Trust
Earned ago 3, 2024 EDT
Google Cloud Generative AI Trailblazer
Earned ago 3, 2024 EDT
Google Security Operations - Deep Dive
Earned lug 27, 2024 EDT
Google Security Operations - Fundamentals
Earned lug 25, 2024 EDT
[DEPRECATED] SOAR Fundamentals
Earned lug 17, 2024 EDT
Mandiant Fundamentals
Earned lug 17, 2024 EDT
Chronicle SIEM Fundamentals
Earned lug 17, 2024 EDT
[DEPRECATED]- Google Cloud Computing Foundations: Cloud Computing Fundamentals - Italiano
Earned lug 12, 2024 EDT
Google API products: A key to modern application development
Earned lug 3, 2024 EDT
Introduzione al ML: elaborazione del linguaggio
Earned lug 3, 2024 EDT
VM Migration for Partners
Earned lug 2, 2024 EDT
Managing Change when Moving to Google Cloud
Earned lug 2, 2024 EDT
Rapid Migration & Modernization Program
Earned giu 26, 2024 EDT
Managing Security in Google Cloud
Earned giu 20, 2024 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned giu 11, 2024 EDT
Hybrid Cloud Infrastructure Foundations with Anthos
Earned giu 5, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned mag 28, 2024 EDT
Introduction to Gemini Enterprise for Customer Experience and Conversational Agents
Earned mag 21, 2024 EDT
Developing a Google SRE Culture
Earned mag 20, 2024 EDT
Gemini for end-to-end SDLC
Earned mag 12, 2024 EDT
Improving developer velocity with Gemini Code Assist
Earned mag 10, 2024 EDT
Gemini for Data Scientists and Analysts
Earned mag 3, 2024 EDT
Gemini for Security Engineers
Earned mag 2, 2024 EDT
Gemini for DevOps Engineers
Earned apr 20, 2024 EDT
Gemini for Network Engineers
Earned apr 19, 2024 EDT
Gemini for Application Developers
Earned apr 18, 2024 EDT
Gemini for Cloud Architects
Earned apr 15, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned apr 13, 2024 EDT
Modernizing Mainframe Applications with Google Cloud
Earned apr 8, 2024 EDT
Search with AI Applications
Earned mar 25, 2024 EDT
Vector Search and Embeddings
Earned mar 18, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned feb 24, 2024 EST
Building Resilient Streaming Systems on Google Cloud Platform
Earned feb 17, 2024 EST
Smart Analytics, Machine Learning, and AI on Google Cloud - Italiano
Earned feb 17, 2024 EST
Creazione di pipeline di dati in batch su Google Cloud
Earned gen 31, 2024 EST
Modernizzazione di data lake e data warehouse con Google Cloud
Earned gen 22, 2024 EST
Preparing for your Professional Data Engineer Journey
Earned gen 11, 2024 EST
Conversational AI on Vertex AI and Dialogflow CX
Earned dic 9, 2023 EST
Introduzione a Google Kubernetes Engine
Earned nov 26, 2023 EST
Infrastruttura Google Cloud elastica: scalabilità e automazione
Earned nov 24, 2023 EST
Preparing for the Professional Cloud Architect Exam - Italiano
Earned nov 14, 2023 EST
ML Pipelines on Google Cloud
Earned ott 24, 2023 EDT
Machine Learning Operations (MLOps): Getting Started
Earned ott 18, 2023 EDT
Recommendation Systems on Google Cloud
Earned ott 17, 2023 EDT
Natural Language Processing on Google Cloud
Earned ott 11, 2023 EDT
Computer Vision Fundamentals with Google Cloud
Earned ott 7, 2023 EDT
Production Machine Learning Systems
Earned ott 1, 2023 EDT
Machine learning in azienda
Earned set 28, 2023 EDT
Feature engineering
Earned set 19, 2023 EDT
Crea, addestra ed esegui il deployment di modelli ML tramite Keras su Google Cloud
Earned set 16, 2023 EDT
Launching into Machine Learning - Italiano
Earned set 14, 2023 EDT
How Google Does Machine Learning
Earned set 8, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals - Italiano
Earned set 5, 2023 EDT
Introduzione all'AI e al machine learning su Google Cloud
Earned set 1, 2023 EDT
Generative AI for Business Leaders
Earned ago 28, 2023 EDT
Text Prompt Engineering Techniques
Earned ago 26, 2023 EDT
Implementing Generative AI with Vertex AI
Earned ago 24, 2023 EDT
Introduction to Generative AI Studio - Italiano
Earned ago 23, 2023 EDT
Create Image Captioning Models - Italiano
Earned ago 22, 2023 EDT
Transformer Models and BERT Model - Italiano
Earned ago 22, 2023 EDT
Encoder-Decoder Architecture - Italiano
Earned ago 22, 2023 EDT
Attention Mechanism - Italiano
Earned ago 21, 2023 EDT
Generative AI Explorer : Vertex AI
Earned lug 30, 2023 EDT
Introduction to Image Generation - Italiano
Earned lug 29, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud - Italiano
Earned lug 29, 2023 EDT
Generative AI Fundamentals
Earned lug 28, 2023 EDT
Introduction to Responsible AI - Italiano
Earned lug 28, 2023 EDT
Introduction to Large Language Models - Italiano
Earned lug 28, 2023 EDT
Introduction to Generative AI - Italiano
Earned lug 27, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads - Italiano
Earned ott 14, 2021 EDT
Progettazione dell'architettura con Google Kubernetes Engine: fondamenti
Earned ott 5, 2021 EDT
Infrastruttura Google Cloud affidabile: progetto e processo
Earned set 22, 2021 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation - Locales
Earned set 20, 2021 EDT