Google Cloud Solutions I: Scaling Your Infrastructure
Earned дек. 16, 2019 EST
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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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
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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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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.
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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.
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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 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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Kubernetes is the most popular container orchestration system, and Google Kubernetes Engine was designed specifically to support managed Kubernetes deployments in Google Cloud. In this course, you will get hands-on practice configuring Docker images, containers, and deploying fully-fledged Kubernetes Engine applications.
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This fundamental-level quest is unique amongst the other quest offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
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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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In this course you will learn how you to harness serious Google Cloud power and infrastructure. The hands-on labs will give you use cases and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your Google Cloud projects to the next level.
Create ML Models with BigQuery ML
Earned окт. 9, 2025 EDT
Working with Notebooks in Vertex AI
Earned окт. 7, 2025 EDT
Prepare Data for ML APIs on Google Cloud
Earned окт. 7, 2025 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned авг. 19, 2025 EDT
Data Warehousing for Partners: Process Data with Dataproc
Earned июля 22, 2025 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned июля 16, 2025 EDT
AI Services and GDC Deployments and Operations
Earned июля 15, 2025 EDT
Compute, Network, and Storage Services Configuration in GDC
Earned июля 11, 2025 EDT
GDC Platform Introduction
Earned июля 8, 2025 EDT
Kubernetes in Google Cloud
Earned дек. 22, 2021 EST
DEPRECATED Cloud Architecture
Earned дек. 21, 2021 EST
Hybrid Cloud Infrastructure Foundations with Anthos
Earned сент. 3, 2020 EDT
Google Cloud Solutions I: Scaling Your Infrastructure
Earned дек. 16, 2019 EST