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Ny Avo Sarobidy Andriatsilavo

Учасник із 2025

Бронзова ліга

Кількість балів: 34198
Machine Learning in the Enterprise Earned січ. 13, 2026 EST
Recommendation Systems on Google Cloud Earned жовт. 31, 2025 EDT
End-to-End Machine Learning with TensorFlow on Google Cloud Earned жовт. 20, 2025 EDT
Build Custom Processors with Document AI Earned вер. 16, 2025 EDT
Document AI: Building a Custom Document Extractor Earned вер. 15, 2025 EDT
Build a Certification Study Guide: PMLE Earned серп. 28, 2025 EDT
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI Earned серп. 28, 2025 EDT
Принципи відповідального використання ШІ для розробників: конфіденційність і безпека Earned серп. 28, 2025 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned серп. 27, 2025 EDT
Create Generative AI Apps on Google Cloud Earned серп. 27, 2025 EDT
Принципи відповідального використання ШІ для розробників: інтерпретованість і прозорість Earned серп. 27, 2025 EDT
Принципи відповідального використання ШІ для розробників: об’єктивність і упередженість Earned серп. 26, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned серп. 25, 2025 EDT
Introduction to Large Language Models - Українська Earned серп. 22, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned серп. 22, 2025 EDT
Machine Learning Operations (MLOps): Getting Started Earned серп. 21, 2025 EDT
Production Machine Learning Systems Earned серп. 21, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI Earned серп. 19, 2025 EDT
Introduction to Generative AI - Українська Earned серп. 19, 2025 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned серп. 18, 2025 EDT
Feature Engineering Earned серп. 12, 2025 EDT
Create ML Models with BigQuery ML Earned лип. 22, 2025 EDT
Working with Notebooks in Vertex AI Earned лип. 21, 2025 EDT
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud Earned лип. 21, 2025 EDT
Scaling with Google Cloud Operations Earned лип. 15, 2025 EDT
Trust and Security with Google Cloud Earned лип. 15, 2025 EDT
Modernize Infrastructure and Applications with Google Cloud Earned лип. 14, 2025 EDT
Innovating with Google Cloud Artificial Intelligence Earned лип. 11, 2025 EDT
Introduction to AI and Machine Learning on Google Cloud Earned лип. 10, 2025 EDT
Exploring Data Transformation with Google Cloud Earned лип. 8, 2025 EDT
Digital Transformation with Google Cloud Earned лип. 8, 2025 EDT
Change Management for Google Workspace Earned лип. 4, 2025 EDT
Workspace Transformation Earned лип. 2, 2025 EDT

This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

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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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One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. It involves building an end-to-end model from data exploration all the way to deploying an ML model and getting predictions from it. This is the first course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Production Machine Learning Systems course.

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This skill badge course is designed to offer hands-on experience through labs, enabling participants to master Document AI for document processing and extraction tasks. By the end of the course, participants will be proficient in creating and testing Document AI processors, customizing document extraction using Document AI Workbench, and building custom processors to tackle real-world document processing challenges.

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This workload aims to upskill Google Cloud partners to perform specific tasks associated with building a Custom Doc Extractor using the Google Cloud AI solution. The following will be addressed: Service: Document AI Task: Extract fields Processors: Custom Document Extractor and Document Splitter Prediction: Using Endpoint to programmatically extract fields

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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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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.

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Під час цього курсу ви ознайомитеся з важливими темами, що стосуються конфіденційності й безпеки в системах ШІ. Ви дізнаєтеся про практичні методи й інструменти, які дають змогу застосувати рекомендації щодо конфіденційності й безпеки в системах ШІ за допомогою продуктів Google Cloud і інструментів із відкритим кодом.

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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.

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Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud? In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application.

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У цьому курсі розглядаються поняття інтерпретованості й прозорості штучного інтелекту, а також їх важливість для розробників. Ви дізнаєтеся про практичні методи й інструменти, які дають змогу досягти інтерпретованості й прозорості даних і моделей штучного інтелекту.

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Під час цього курсу ви зможете ознайомитися з концепціями відповідального підходу й принципами щодо штучного інтелекту. Ви дізнаєтеся про практичні методи виявлення об’єктивності й упередженості в роботі ШІ та технологій машинного навчання, а також ознайомитеся зі способами мінімізувати упередженість. У курсі розглядаються практичні методи й інструменти для впровадження відповідального підходу до ШІ за допомогою продуктів Google Cloud і інструментів із відкритим кодом.

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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production. Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.

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У цьому ознайомлювальному курсі мікронавчання ви дізнаєтеся, що таке великі мовні моделі, де вони використовуються і як підвищити їх ефективність коригуванням запитів. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучного інтелекту.

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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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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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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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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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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке генеративний штучний інтелект, як він використовується й чим відрізняється від традиційних методів машинного навчання. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучногоінтелекту.

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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.

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Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Dataproc, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API.

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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task. Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts. 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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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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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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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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This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.

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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences. “Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible. 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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There's much excitement about cloud technology and digital transformation, but often many unanswered questions. For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin? If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. 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 is part of the Cloud Digital Leader learning path.

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This course is for deployment personnel of Google Cloud and its partner organizations who are tasked with managing cultural change and skill gaps of customers adopting Google Workspace, engaging sponsors and enlisting the support of customer stakeholders, communicating the anticipated changes to the customer and their users. It will guide you through the Workspace change management methodology and all phases of the customer journey.

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This course on workspace transformation guides participants through modules designed to optimize and transform business environments using Google Workspace. It covers the Google Workspace Customer Success Methodology, provisioning processes, authentication and system access, mail routing, migration strategies, and coexistence planning to ensure a seamless transition and effective implementation.

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