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このコースでは、Google Cloud で最先端の ML パイプラインに携わっている ML エンジニアおよびトレーナーたちから知識を吸収することができます。 最初のいくつかのモジュールで、ML パイプラインとメタデータの管理用 TensorFlow を基盤とする Google の本番環境向け機械学習プラットフォーム TensorFlow Extended（TFX）について説明します。パイプラインのコンポーネントについて、そして TFX を使用したパイプラインのオーケストレーションについて学習します。また、継続的インテグレーションと継続的デプロイを通じたパイプラインの自動化の方法と、ML メタデータの管理方法についても学習します。その後、焦点を変えて、TensorFlow、PyTorch、Scikit Learn、XGBoost などの複数の ML フレームワーク全体にわたる ML パイプラインの自動化と再利用の方法について説明します。 さらに、Google Cloud のもう 1 つのツール、Cloud Composer を継続的なトレーニング パイプラインのオーケストレーションに活用する方法についても学習します。最後は、MLflow を使用して機械学習の完全なライフサイクルを管理する方法の解説で締めくくります。
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What's inside
Syllabus
はじめに
このモジュールではコースの概要を説明します。
TFX パイプラインの紹介
このモジュールでは、TensorFlow Extended（TFX）を紹介し、TFX のコンセプトとコンポーネントについて説明します
Read more
Syllabus
はじめに
このモジュールではコースの概要を説明します。
TFX パイプラインの紹介
このモジュールでは、TensorFlow Extended（TFX）を紹介し、TFX のコンセプトとコンポーネントについて説明します
Read more
TFX によるパイプライン オーケストレーション
このモジュールの内容
TFX パイプラインのカスタム コンポーネントと CI / CD
TFX におけるメタデータ
このモジュールでは、アーティファクト管理のための TFX メタデータの活用について説明します。
複数の SDK、KubeFlow および AI Platform Pipelines を使用した継続的なトレーニング
このモジュールでは、複数の SDK、KubeFlow および AI Platform Pipelines を使用した継続的なトレーニングについて説明します。
Cloud Composer を使用した継続的なトレーニング
このモジュールでは、Cloud Composer を使用した継続的なトレーニングについて説明します。
MLflow を使用した ML パイプライン
このモジュールでは、MLflow とそのコンポーネントを紹介します。
まとめ
このモジュールでは、本コースで取り上げた内容を振り返ります
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
適合する聴衆は、機械学習の背景がある人です。
Google Cloud の講師による指導で信頼性が高いです。
Save this course
Save ML Pipelines on Google Cloud - 日本語版 to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in ML Pipelines on Google Cloud - 日本語版 with these
activities:
Review recent advances in TensorFlow
Show steps
TensorFlow has undergone significant changes over the years and some skills may have become rusty. Refresh your knowledge of its latest advancements to enhance your learning in this course.
Browse courses on
TensorFlow
Show steps
-
Visit the TensorFlow website and explore the latest releases and updates.
-
Read blog posts and articles on recent advances in TensorFlow.
-
Attend a TensorFlow webinar or workshop.
Practice building ML pipelines using TFX
Show steps
Gain practical experience by building and running ML pipelines using TFX. This will solidify your understanding of the pipeline components and their interactions.
Browse courses on
TensorFlow Extended
Show steps
-
Follow the TFX tutorial to create a basic ML pipeline.
-
Experiment with different pipeline components and configurations.
-
Deploy your pipeline to a production environment.
Join a study group or online forum for ML pipeline development
Show steps
Connect with other ML engineers and practitioners to discuss best practices, share knowledge, and troubleshoot issues related to ML pipeline development.
Show steps
-
Find an online forum or study group dedicated to ML pipeline development.
-
Introduce yourself and ask questions related to the course material.
-
Participate in discussions and share your own experiences.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore advanced techniques for ML pipeline automation and re-use
Show steps
Learn advanced techniques for automating and re-using ML pipelines using tools like Cloud Composer. This will enable you to streamline your ML development process and save time.
Browse courses on
Cloud Composer
Show steps
-
Watch a tutorial on using Cloud Composer for ML pipeline automation.
-
Read documentation on MLflow and its components.
-
Experiment with different techniques and tools for ML pipeline automation and re-use.
Mentor a junior ML engineer in ML pipeline development
Show steps
Share your knowledge and experience by mentoring a junior ML engineer in ML pipeline development. This will not only benefit the mentee but also reinforce your understanding of the subject.
Show steps
-
Identify a junior ML engineer who is interested in learning about ML pipeline development.
-
Set up regular mentoring sessions to provide guidance and support.
-
Share resources, provide feedback on their work, and answer their questions.
Participate in a Kaggle competition related to ML pipelines
Show steps
Challenge yourself and apply your skills by participating in a Kaggle competition focused on ML pipelines. This will not only test your knowledge but also provide valuable hands-on experience.
Show steps
-
Identify a Kaggle competition that aligns with the topics covered in this course.
-
Build an ML pipeline to solve the competition problem.
-
Submit your solution and track your progress.
Contribute to an open-source ML pipeline project
Show steps
Make a meaningful contribution to the ML community by contributing to an open-source ML pipeline project. This will not only enhance your skills but also give you valuable experience in collaborative development.
Show steps
-
Identify an open-source ML pipeline project that aligns with your interests.
-
Read the project documentation and familiarize yourself with its codebase.
-
Identify an area where you can make a contribution.
-
Submit a pull request with your proposed changes.
Build a complete ML pipeline for a real-world problem
Show steps
Apply your knowledge and skills to a practical project. Build a complete ML pipeline for a real-world problem, showcasing your understanding of the entire ML pipeline development process.
Show steps
-
Define the problem and gather the necessary data.
-
Design and implement the ML pipeline using appropriate tools and techniques.
-
Train and evaluate the pipeline.
-
Deploy the pipeline to production and monitor its performance.
Review recent advances in TensorFlow
Show steps
TensorFlow has undergone significant changes over the years and some skills may have become rusty. Refresh your knowledge of its latest advancements to enhance your learning in this course.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Visit the TensorFlow website and explore the latest releases and updates.
- Read blog posts and articles on recent advances in TensorFlow.
- Attend a TensorFlow webinar or workshop.
Practice building ML pipelines using TFX
Show steps
Gain practical experience by building and running ML pipelines using TFX. This will solidify your understanding of the pipeline components and their interactions.
Browse courses on
TensorFlow Extended
Show steps
Show steps
Show steps
- Follow the TFX tutorial to create a basic ML pipeline.
- Experiment with different pipeline components and configurations.
- Deploy your pipeline to a production environment.
Join a study group or online forum for ML pipeline development
Show steps
Connect with other ML engineers and practitioners to discuss best practices, share knowledge, and troubleshoot issues related to ML pipeline development.
Show steps
Show steps
Show steps
- Find an online forum or study group dedicated to ML pipeline development.
- Introduce yourself and ask questions related to the course material.
- Participate in discussions and share your own experiences.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore advanced techniques for ML pipeline automation and re-use
Show steps
Learn advanced techniques for automating and re-using ML pipelines using tools like Cloud Composer. This will enable you to streamline your ML development process and save time.
Browse courses on
Cloud Composer
Show steps
Show steps
Show steps
- Watch a tutorial on using Cloud Composer for ML pipeline automation.
- Read documentation on MLflow and its components.
- Experiment with different techniques and tools for ML pipeline automation and re-use.
Mentor a junior ML engineer in ML pipeline development
Show steps
Share your knowledge and experience by mentoring a junior ML engineer in ML pipeline development. This will not only benefit the mentee but also reinforce your understanding of the subject.
Show steps
Show steps
Show steps
- Identify a junior ML engineer who is interested in learning about ML pipeline development.
- Set up regular mentoring sessions to provide guidance and support.
- Share resources, provide feedback on their work, and answer their questions.
Participate in a Kaggle competition related to ML pipelines
Show steps
Challenge yourself and apply your skills by participating in a Kaggle competition focused on ML pipelines. This will not only test your knowledge but also provide valuable hands-on experience.
Show steps
Show steps
Show steps
- Identify a Kaggle competition that aligns with the topics covered in this course.
- Build an ML pipeline to solve the competition problem.
- Submit your solution and track your progress.
Contribute to an open-source ML pipeline project
Show steps
Make a meaningful contribution to the ML community by contributing to an open-source ML pipeline project. This will not only enhance your skills but also give you valuable experience in collaborative development.
Show steps
Show steps
Show steps
- Identify an open-source ML pipeline project that aligns with your interests.
- Read the project documentation and familiarize yourself with its codebase.
- Identify an area where you can make a contribution.
- Submit a pull request with your proposed changes.
Build a complete ML pipeline for a real-world problem
Show steps
Apply your knowledge and skills to a practical project. Build a complete ML pipeline for a real-world problem, showcasing your understanding of the entire ML pipeline development process.
Show steps
Show steps
Show steps
- Define the problem and gather the necessary data.
- Design and implement the ML pipeline using appropriate tools and techniques.
- Train and evaluate the pipeline.
- Deploy the pipeline to production and monitor its performance.
Career center
Learners who complete ML Pipelines on Google Cloud - 日本語版 will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers are the backbone of the data science industry, designing data management systems that power machine learning models. In this course, you will gain a strong foundation in TensorFlow Extended (TFX), Google's production-grade machine learning platform, and learn best practices for orchestrating ML pipelines, ensuring data integrity and quality.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models. This course will provide you with the skills to build robust ML pipelines on Google Cloud, using TFX and other industry-leading tools. You will learn how to automate pipeline execution, monitor model performance, and ensure compliance with industry regulations.
Data Scientist
Data Scientist
Data Scientists leverage machine learning and statistical models to extract insights from data. This course will teach you how to construct ML pipelines using TFX and other popular frameworks, enabling you to effectively manage data, build models, and analyze results. The focus on automation and continuous integration will help you streamline your workflow and accelerate your data science projects.
Software Engineer (Machine Learning)
Software Engineer (Machine Learning)
Software Engineers specializing in Machine Learning design and implement software solutions for ML applications. This course will provide you with a comprehensive understanding of ML pipelines, enabling you to bridge the gap between data science and software engineering. You will learn how to integrate ML models into production systems, ensuring reliability, scalability, and maintainability.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing infrastructure and services. This course will help you develop the skills necessary to design and implement ML pipelines on Google Cloud. You will learn how to leverage managed services such as AI Platform Pipelines and Cloud Composer to automate pipeline execution and ensure high availability and scalability.
Data Analyst
Data Analyst
Data Analysts transform raw data into actionable insights. This course will provide you with the foundation to build ML pipelines that can automate data preparation, feature engineering, and model training. You will learn how to use TFX and other tools to manage data and metadata, ensuring the integrity and reliability of your analysis.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to improve business decision-making. This course will teach you how to build ML pipelines that can automate data analysis and reporting. You will learn how to use TFX and other tools to manage data and metadata, ensuring the accuracy and consistency of your analysis.
Product Manager (Machine Learning)
Product Manager (Machine Learning)
Product Managers specializing in Machine Learning are responsible for defining and delivering ML-powered products. This course will provide you with a comprehensive understanding of ML pipelines, enabling you to bridge the gap between business requirements and technical implementation. You will learn how to define and prioritize ML use cases, set performance metrics, and ensure product alignment with business goals.
Research Scientist: Machine Learning
Research Scientist: Machine Learning
Research Scientists specializing in Machine Learning develop and evaluate new ML algorithms and techniques. This course will provide you with a strong foundation in TFX and other industry-leading ML tools, enabling you to contribute to the advancement of the field. You will learn how to design and conduct ML experiments, analyze results, and publish your findings in peer-reviewed journals.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course will provide you with the skills to build ML pipelines that can automate data preparation, feature engineering, and model training. You will learn how to use TFX and other tools to manage data and metadata, ensuring the integrity and reliability of your analysis.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to draw meaningful conclusions. This course will provide you with a strong foundation in TFX and other industry-leading ML tools, enabling you to apply statistical techniques to large-scale datasets. You will learn how to design and conduct ML experiments, analyze results, and communicate your findings effectively.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business and industry. This course will provide you with the skills to build ML pipelines that can automate data preparation, feature engineering, and model training. You will learn how to use TFX and other tools to manage data and metadata, ensuring the integrity and reliability of your analysis.
Data Management Analyst
Data Management Analyst
Data Management Analysts design and implement systems to manage and protect data assets. This course will provide you with a strong foundation in TFX and other industry-leading ML tools, enabling you to manage ML data efficiently and securely. You will learn how to design and implement data governance policies, protect data from unauthorized access, and ensure compliance with regulations.
IT Architect
IT Architect
IT Architects design and implement IT infrastructure and services. This course will provide you with a comprehensive understanding of ML pipelines, enabling you to design and implement ML solutions that meet the scalability, reliability, and security requirements of your organization. You will learn how to integrate ML models into existing IT systems, ensuring seamless operation and data integrity.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This course may be useful for Software Developers who want to build ML pipelines as part of their software applications. You will learn how to use TFX and other industry-leading ML tools to integrate ML models into your applications, ensuring efficient and reliable performance.
For more career information including salaries, visit:
OpenCourser.com/course/0vpzec/ml
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
ML Pipelines on Google Cloud - 日本語版.
Provides a comprehensive overview of deep learning with Python, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a practical guide to building and deploying production-ready ML pipelines, with a focus on using open-source tools and technologies. It covers topics such as data preparation, model training, evaluation, and deployment.
Provides a comprehensive overview of machine learning with R, covering topics such as data preprocessing, model training, and evaluation.
Save
Provides a comprehensive overview of data mining, covering topics such as data preprocessing, feature selection, and model evaluation.
Provides a comprehensive overview of linear models, covering topics such as regression, ANOVA, and time series analysis.
この本は、機械学習モデルの継続的デリバリーに関する包括的なガイドで、自動化されたパイプライン、継続的インテグレーション、継続的デプロイメントについて説明しています。このコースの CI/CD に関するモジュールを補完します。
この本は、Python を使用した機械学習の包括的な入門書です。このコースの基礎となる機械学習の概念を理解するのに役立ちます。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0vpzec/ml
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Effort
3 weeks of study, 6-8 hours/week
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
日本語
Transcript
English
Good to know
適合する聴衆は、機械学習の背景がある人です。
Google Cloud の講師による指導で信頼性が高いです。
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