TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
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Google Cloud での DevOps ワークフローの実装 コースの中級スキルバッジを獲得できるアクティビティを修了すると、 Cloud Source Repositories を使用した Git リポジトリの作成、 Google Kubernetes Engine（GKE）上でのデプロイのリリース、管理、スケール、 コンテナ イメージのビルドと GKE へのデプロイを自動化する CI / CD パイプラインの設計といったスキルを実証できます。
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Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on Google Cloud.
Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.
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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.
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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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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.
BigQuery のデータから分析情報を引き出す
Earned 4月 16, 2023 EDT
Google Cloud ネットワークにおけるデプロイの自動化とトラフィックの管理
Earned 4月 15, 2023 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned 4月 15, 2023 EDT
Google Cloud での DevOps ワークフローの実装
Earned 4月 14, 2023 EDT
Advanced ML: ML Infrastructure
Earned 4月 14, 2023 EDT
クラウド エンジニアリング
Earned 4月 14, 2023 EDT
Firebase を使用したサーバーレス アプリの開発
Earned 4月 12, 2023 EDT
Data Catalog Fundamentals
Earned 4月 10, 2023 EDT
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications
Earned 4月 9, 2023 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned 4月 9, 2023 EDT
Google Cloud Solutions I: Scaling Your Infrastructure
Earned 4月 8, 2023 EDT
Google Cloud におけるアプリ開発環境の設定
Earned 4月 8, 2023 EDT
Compute Engine での Cloud Load Balancing の実装
Earned 4月 6, 2023 EDT
Google Cloud でのウェブサイトの構築
Earned 4月 6, 2023 EDT