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Google Cloud Training
このコースでは、Google Cloud 上で本番環境の ML システムをデプロイ、評価、モニタリング、運用するための MLOps ツールとベスト プラクティスについて説明します。MLOps は、本番環境 ML システムのデプロイ、テスト、モニタリング、自動化に重点を置いた規範です。
受講者は、SDK レイヤで Vertex AI Feature Store のストリーミング取り込みを使用する実践的な演習を受けられます。
Register for this course and see more details by visiting:
OpenCourser.com/course/32k4e1/mlops
What's inside
Syllabus
「Machine Learning Operations (MLOps) with Vertex AI: Manage Features」へようこそ
コースの概要。
Vertex AI Feature Store の概要
Vertex AI とその MLOps 機能。データに関する主な課題とそれらの課題を軽減するためのソリューション。
Read more
Syllabus
「Machine Learning Operations (MLOps) with Vertex AI: Manage Features」へようこそ
コースの概要。
Vertex AI Feature Store の概要
Vertex AI とその MLOps 機能。データに関する主な課題とそれらの課題を軽減するためのソリューション。
Read more
「Machine Learning Operations (MLOps) with Vertex AI: Manage Features」の詳細
Vertex AI Feature Store の主な機能。
まとめ
このコースのまとめ。
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers tools and best practices that are relevant to a production environment, which may be highly relevant to many industries
Involves an external provider, which may increase the quality of the course
Involves a known provider, which may increase the quality of the course
Taught by industry experts, which is a mark of quality
Emphasizes the importance of deploying as well as testing and monitoring, which may provide a well rounded perspective on MLOps
Introduces Vertex AI Feature Store, which may provide insights into the most up-to-date tools and techniques
Save this course
Save MLOps with Vertex AI: Manage Features - 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 MLOps with Vertex AI: Manage Features - 日本語版 with these
activities:
Review Prerequisites
Show steps
Strengthen the foundation for the course by reviewing prerequisite topics, ensuring a solid understanding of essential concepts.
Browse courses on
Python
Show steps
-
Review Python basics and data structures.
-
Revisit fundamental machine learning concepts.
-
Recall key aspects of Google Cloud Platform, particularly relevant services.
Review Vertex AI Feature Store Architecture Tutorial
Show steps
Explore the architecture of Vertex AI Feature Store to understand its components and their interrelationships, reinforcing the course concepts.
Browse courses on
Vertex AI Feature Store
Show steps
-
Navigate to the Vertex AI Feature Store Architecture Tutorial.
-
Read through the tutorial, paying attention to the architecture diagram and explanations.
-
Make notes of key concepts and components of the Feature Store.
-
Identify how the components interact and contribute to the overall functioning of the Feature Store.
-
Consider how this knowledge applies to the course material and machine learning systems in general.
Practice Deploying ML Models using Vertex Prediction Service
Show steps
Enhance understanding of model deployment by practicing with Vertex Prediction Service, solidifying the course concepts and practical skills.
Show steps
-
Train an ML model using TensorFlow or scikit-learn.
-
Deploy the trained model to Vertex Prediction Service.
-
Create and manage endpoints for serving the deployed model.
-
Test and evaluate the deployed model using real-world data.
-
Monitor the deployed model and make adjustments as needed.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement Streaming Ingestion from BigQuery to Vertex AI Feature Store
Show steps
Practice streaming data ingestion into a Vertex AI Feature Store, applying the concepts learned in the course and strengthening hands-on skills.
Browse courses on
Streaming Ingestion
Show steps
-
Set up a BigQuery source dataset.
-
Configure streaming ingestion from BigQuery to Vertex AI Feature Store.
-
Create a Feature Store entity and import data from BigQuery.
-
Verify successful data ingestion by querying the Feature Store.
Develop a Feature Store Design Document
Show steps
Solidify understanding of feature store design by creating a comprehensive document, applying course concepts to a practical scenario.
Show steps
-
Define the business use case and objectives for the feature store.
-
Identify the data sources and their characteristics.
-
Design the feature store schema and data pipeline.
-
Document the data governance and security measures.
-
Present the feature store design document for review.
Attend Vertex AI Feature Store Workshop
Show steps
Expand knowledge and practical skills by attending a workshop focused on Vertex AI Feature Store, enhancing the course material through hands-on learning.
Browse courses on
Feature Engineering
Show steps
-
Register and attend the Vertex AI Feature Store workshop.
-
Actively participate in the workshop sessions.
-
Network with other attendees and learn from their experiences.
-
Apply workshop learnings to your own projects and understanding of the course material.
Compile Resource Collection on Vertex AI MLOps
Show steps
Enhance learning and stay up-to-date by compiling a comprehensive collection of resources related to Vertex AI MLOps, providing a valuable reference tool.
Browse courses on
Machine Learning Operations
Show steps
-
Search for and gather resources from various sources.
-
Organize and categorize the resources based on topic and relevance.
-
Create a central repository or document for easy access.
-
Share the compilation with peers and the learning community.
Contribute to Open-Source Project on Feature Engineering
Show steps
Deepen understanding and gain practical experience in feature engineering by contributing to an open-source project, applying course concepts in a real-world setting.
Browse courses on
Feature Engineering
Show steps
-
Identify an open-source project related to feature engineering.
-
Read the project's documentation and contribute code.
-
Engage with the project's community and learn from others.
-
Link your contributions to the relevant course concepts.
Review Prerequisites
Show steps
Strengthen the foundation for the course by reviewing prerequisite topics, ensuring a solid understanding of essential concepts.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python basics and data structures.
- Revisit fundamental machine learning concepts.
- Recall key aspects of Google Cloud Platform, particularly relevant services.
Review Vertex AI Feature Store Architecture Tutorial
Show steps
Explore the architecture of Vertex AI Feature Store to understand its components and their interrelationships, reinforcing the course concepts.
Browse courses on
Vertex AI Feature Store
Show steps
Show steps
Show steps
- Navigate to the Vertex AI Feature Store Architecture Tutorial.
- Read through the tutorial, paying attention to the architecture diagram and explanations.
- Make notes of key concepts and components of the Feature Store.
- Identify how the components interact and contribute to the overall functioning of the Feature Store.
- Consider how this knowledge applies to the course material and machine learning systems in general.
Practice Deploying ML Models using Vertex Prediction Service
Show steps
Enhance understanding of model deployment by practicing with Vertex Prediction Service, solidifying the course concepts and practical skills.
Show steps
Show steps
Show steps
- Train an ML model using TensorFlow or scikit-learn.
- Deploy the trained model to Vertex Prediction Service.
- Create and manage endpoints for serving the deployed model.
- Test and evaluate the deployed model using real-world data.
- Monitor the deployed model and make adjustments as needed.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement Streaming Ingestion from BigQuery to Vertex AI Feature Store
Show steps
Practice streaming data ingestion into a Vertex AI Feature Store, applying the concepts learned in the course and strengthening hands-on skills.
Browse courses on
Streaming Ingestion
Show steps
Show steps
Show steps
- Set up a BigQuery source dataset.
- Configure streaming ingestion from BigQuery to Vertex AI Feature Store.
- Create a Feature Store entity and import data from BigQuery.
- Verify successful data ingestion by querying the Feature Store.
Develop a Feature Store Design Document
Show steps
Solidify understanding of feature store design by creating a comprehensive document, applying course concepts to a practical scenario.
Show steps
Show steps
Show steps
- Define the business use case and objectives for the feature store.
- Identify the data sources and their characteristics.
- Design the feature store schema and data pipeline.
- Document the data governance and security measures.
- Present the feature store design document for review.
Attend Vertex AI Feature Store Workshop
Show steps
Expand knowledge and practical skills by attending a workshop focused on Vertex AI Feature Store, enhancing the course material through hands-on learning.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Register and attend the Vertex AI Feature Store workshop.
- Actively participate in the workshop sessions.
- Network with other attendees and learn from their experiences.
- Apply workshop learnings to your own projects and understanding of the course material.
Compile Resource Collection on Vertex AI MLOps
Show steps
Enhance learning and stay up-to-date by compiling a comprehensive collection of resources related to Vertex AI MLOps, providing a valuable reference tool.
Browse courses on
Machine Learning Operations
Show steps
Show steps
Show steps
- Search for and gather resources from various sources.
- Organize and categorize the resources based on topic and relevance.
- Create a central repository or document for easy access.
- Share the compilation with peers and the learning community.
Contribute to Open-Source Project on Feature Engineering
Show steps
Deepen understanding and gain practical experience in feature engineering by contributing to an open-source project, applying course concepts in a real-world setting.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Identify an open-source project related to feature engineering.
- Read the project's documentation and contribute code.
- Engage with the project's community and learn from others.
- Link your contributions to the relevant course concepts.
Career center
Learners who complete MLOps with Vertex AI: Manage Features - 日本語版 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. They use statistical and machine learning techniques to build models that can predict future trends and outcomes. This course can help you develop the skills you need to become a successful Data Scientist by providing you with a foundation in MLOps, which is the practice of deploying, evaluating, and monitoring ML systems in production. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and deploying ML systems. They work closely with Data Scientists to develop and implement ML models. This course can help you develop the skills you need to become a successful Machine Learning Engineer by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that stores and processes data. They work closely with Data Scientists and Machine Learning Engineers to ensure that data is available and accessible for analysis and modeling. This course can help you develop the skills you need to become a successful Data Engineer by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They work closely with Data Scientists and Machine Learning Engineers to implement ML models into production systems. This course can help you develop the skills you need to become a successful Software Engineer by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business needs and developing solutions to improve business processes. They work closely with Data Scientists and Machine Learning Engineers to ensure that ML models are aligned with business objectives. This course can help you develop the skills you need to become a successful Business Analyst by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. They work closely with Data Scientists and Machine Learning Engineers to ensure that ML models are integrated into products in a way that meets user needs. This course can help you develop the skills you need to become a successful Product Manager by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Data Architect
Data Architect
Data Architects are responsible for designing and managing data architectures. They work closely with Data Scientists and Machine Learning Engineers to ensure that data is stored and processed in a way that supports ML modeling. This course can help you develop the skills you need to become a successful Data Architect by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Systems Engineer
Systems Engineer
Systems Engineers are responsible for designing and managing IT systems. They work closely with Data Scientists and Machine Learning Engineers to ensure that ML models are deployed and operated in a reliable and scalable manner. This course can help you develop the skills you need to become a successful Systems Engineer by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They work closely with Data Scientists and Machine Learning Engineers to ensure that data is stored and processed in a way that supports ML modeling. This course can help you develop the skills you need to become a successful Database Administrator by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Data Analyst
Data Analyst
Data Analysts are responsible for analyzing data to identify trends and patterns. They work closely with Data Scientists and Machine Learning Engineers to provide insights that can be used to improve business decisions. This course can help you develop the skills you need to become a successful Data Analyst by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts are responsible for testing and validating software applications. They work closely with Data Scientists and Machine Learning Engineers to ensure that ML models are accurate and reliable. This course can help you develop the skills you need to become a successful Quality Assurance Analyst by providing you with a foundation in MLOps. You will learn how to use Vertex AI Feature Store to manage features, which are the building blocks of ML models.
Project Manager
Project Manager
Project Managers are responsible for planning and managing projects. They work closely with Data Scientists and Machine Learning Engineers to ensure that ML projects are completed on time and within budget. This course may help you develop some of the skills you need to become a successful Project Manager by providing you with a foundation in MLOps. However, you may want to consider taking additional courses that focus on project management.
Technical Writer
Technical Writer
Technical Writers are responsible for creating documentation for software applications. They work closely with Data Scientists and Machine Learning Engineers to document ML models and their使用方法. This course may help you develop some of the skills you need to become a successful Technical Writer by providing you with a foundation in MLOps. However, you may want to consider taking additional courses that focus on technical writing.
Sales Engineer
Sales Engineer
Sales Engineers are responsible for selling software applications. They work closely with Data Scientists and Machine Learning Engineers to demonstrate ML models to potential customers. This course may help you develop some of the skills you need to become a successful Sales Engineer by providing you with a foundation in MLOps. However, you may want to consider taking additional courses that focus on sales.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with software applications. They work closely with Data Scientists and Machine Learning Engineers to resolve issues and provide support. This course may help you develop some of the skills you need to become a successful Customer Success Manager by providing you with a foundation in MLOps. However, you may want to consider taking additional courses that focus on customer success.
For more career information including salaries, visit:
OpenCourser.com/course/32k4e1/mlops
Reading list
We've selected 12 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
MLOps with Vertex AI: Manage Features - 日本語版.
Provides a broad overview of feature engineering principles and techniques, covering topics such as data preparation, feature transformations, and feature selection. It valuable resource for practitioners looking to improve their feature engineering skills.
Provides a practical guide to building and deploying machine learning models using Scikit-Learn, Keras, and TensorFlow 3. It covers topics such as data preparation, model selection, and hyperparameter tuning, and valuable resource for practitioners looking to get started with machine learning.
Provides a comprehensive overview of deep learning concepts and techniques, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for practitioners looking to gain a deeper understanding of deep learning.
Save
Provides a theoretical foundation for machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for practitioners looking to gain a deeper understanding of the underlying principles of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as statistical pattern recognition, Bayesian inference, and neural networks. It valuable resource for practitioners looking to gain a broad understanding of the field.
Provides a probabilistic perspective on machine learning, covering topics such as Bayesian inference, graphical models, and reinforcement learning. It valuable resource for practitioners looking to gain a deeper understanding of the underlying principles of machine learning.
Provides a comprehensive overview of machine learning concepts and techniques, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for practitioners looking to gain a broad understanding of the field.
Provides a gentle introduction to deep learning concepts and techniques, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for practitioners looking to get started with deep learning.
Provides a very basic introduction to machine learning concepts and techniques, covering topics such as supervised learning and unsupervised learning. It valuable resource for practitioners looking to get a very basic understanding of the field.
Provides a comprehensive overview of machine learning concepts and techniques using the R programming language. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for practitioners looking to use R for machine learning.
Provides a gentle introduction to machine learning concepts and techniques using the Python programming language. It covers topics such as supervised learning and unsupervised learning. It valuable resource for practitioners looking to get started with machine learning using Python.
Provides a practical guide to machine learning concepts and techniques, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for practitioners looking to get started with machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/32k4e1/mlops
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Transcript
日本語
Good to know
Covers tools and best practices that are relevant to a production environment, which may be highly relevant to many industries
Involves an external provider, which may increase the quality of the course
Involves a known provider, which may increase the quality of the course
Taught by industry experts, which is a mark of quality
Emphasizes the importance of deploying as well as testing and monitoring, which may provide a well rounded perspective on MLOps
Introduces Vertex AI Feature Store, which may provide insights into the most up-to-date tools and techniques
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