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このコースには、ML ワークフローに対する実践的なアプローチが含まれています。ML チームが直面しているいくつかの ML ビジネス要件とユースケースに関するケーススタディの方法を紹介します。ML チームは、データの管理とガバナンスに必要なツールを理解し、Dataflow と Dataprep の概要を提供することから前処理タスクに BigQuery を使用することまで、データの前処理に最適なアプローチを検討する必要があります。

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このコースには、ML ワークフローに対する実践的なアプローチが含まれています。ML チームが直面しているいくつかの ML ビジネス要件とユースケースに関するケーススタディの方法を紹介します。ML チームは、データの管理とガバナンスに必要なツールを理解し、Dataflow と Dataprep の概要を提供することから前処理タスクに BigQuery を使用することまで、データの前処理に最適なアプローチを検討する必要があります。

チームには、2 つの具体的なユースケースに対して機械学習モデルを構築するための 3 つのオプションが提示されます。このコースでは、チームが目的を達成するために、AutoML、BigQuery ML、またはカスタム トレーニングを使用する理由を説明します。さらに、カスタム トレーニングについても深く掘り下げます。コード構造のトレーニング、ストレージ、大規模なデータセットの読み込みからトレーニング済みモデルのエクスポートまで、カスタム トレーニングの要件について説明します。

Docker の知識がほとんどなくてもコンテナ イメージを構築できる、カスタム トレーニングの機械学習モデルを構築します。

ケーススタディ チームは、Vertex Vizier を使用したハイパーパラメータの調整と、これを使用してモデルのパフォーマンスを改善する方法を検証します。モデル改善についての理解を深めるために、理論についても詳しく考察します。正則化、スパース性の扱いなど、数多くある重要なコンセプトと原則について説明します。最後に、予測とモデル モニタリングの概要と、ML モデルを管理するための Vertex AI の活用方法について説明します。

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What's inside

Syllabus

Module 0: はじめに
このモジュールでは、コースの概要と目標を説明します。
Module 1: ML に関する企業のワークフローの把握
このモジュールでは、ML に関する企業のワークフローの概要と各ステップの目的を説明します。
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Module 2: 企業のデータ
このモジュールでは、以下の Google による企業のデータ管理とガバナンスのツールを確認します。Feature Store、Data Catalog、Dataplex、Analytics Hub
Module 3: 機械学習とカスタム トレーニングの科学
このモジュールでは、機械学習とニューラル ネットワークの理論と実践を確認します。また、Vertex AI を使用してカスタム ML モデルをトレーニングする方法についても説明します。
Module 4: Vertex Vizier のハイパーパラメータ調整
このモジュールでは、Vertex AI Vizier を使用してハイパーパラメータ調整を行う方法を説明します。
Module 5: Vertex AI を使用した予測とモデルのモニタリング
このモジュールでは Vertex AI 予測およびモデルのモニタリングについて説明します。まず、ビルド済みコンテナまたはカスタム コンテナを使用したバッチ予測やオンライン予測について説明し、次に ML モデルのパフォーマンスを管理するために役立つサービスであるモデルのモニタリングについて確認します。
Module 6: Vertex AI Pipelines
このモジュールでは Vertex AI Pipelines と、そのパイプラインを構築して ML ワークフローをオーケストレートする方法について説明します。
Module 7: ML 開発のベスト プラクティス
このモジュールでは、Vertex AI でのさまざまな機械学習プロセスのベスト プラクティスを確認します。
Module 8: コースのまとめ
このモジュールは、「Machine Learning in the Enterprise」コースのまとめです。
Module 9: シリーズのまとめ
このモジュールは、「Machine Learning on Google Cloud 日本語版」コースシリーズのまとめです。

Good to know

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指標調整に Vertex Vizier を活用しモデルのパフォーマンス向上を検証します。
Google による企業向けデータ管理とガバナンスのツールについて学び、データの前処理に最適なアプローチを検討します。
ニューラル ネットワークの基礎および実践、カスタム モデルのトレーニングについて学習します。

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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 Machine Learning in the Enterprise - 日本語版 with these activities:
Set up a Cloud environment
Setting up a Cloud environment will help you familiarize yourself with the tools and services you'll be using throughout the course.
Browse courses on Cloud Environment
Show steps
  • Create a Google Cloud account
  • Set up a project and enable billing
  • Configure the Google Cloud SDK and authenticate your account
Participate in a peer discussion forum
Participating in a peer discussion forum will allow you to connect with other students and get feedback on your work.
Browse courses on Collaboration
Show steps
  • Join the discussion forum for the course
  • Introduce yourself and ask questions
  • Respond to other students' questions and comments
  • Share your own insights and experiences
Practice data preparation tasks
Practicing data preparation tasks will help you develop the skills you need to prepare your data for modeling.
Browse courses on Data Preparation
Show steps
  • Load data from a CSV file into BigQuery
  • Clean the data by removing duplicate rows and null values
  • Transform the data by creating new features and normalizing values
  • Save the prepared data to a new BigQuery table
Four other activities
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Show all seven activities
Complete the Coursera tutorial on AutoML Tables
Completing the Coursera tutorial on AutoML Tables will give you hands-on experience with using AutoML to train and deploy machine learning models.
Show steps
  • Create a new AutoML Tables dataset
  • Import your prepared data into the dataset
  • Train an AutoML Tables model
  • Deploy the model and make predictions
Build a custom machine learning model using Vertex AI
Building a custom machine learning model using Vertex AI will give you the opportunity to apply your skills and knowledge to a real-world problem.
Browse courses on Vertex AI
Show steps
  • Define the problem and gather data
  • Preprocess the data and split it into training and testing sets
  • Choose a machine learning algorithm and train the model
  • Evaluate the model's performance and make any necessary adjustments
  • Deploy the model and monitor its performance
Mentor a junior student in the course
Mentoring a junior student in the course will help you solidify your understanding of the material and develop your leadership skills.
Browse courses on Mentoring
Show steps
  • Identify a junior student who could benefit from your help
  • Set up a regular meeting time to discuss the course material and answer any questions
  • Provide feedback on the student's work and offer encouragement
  • Help the student prepare for exams and assignments
Create a blog post or article on a topic related to the course
Creating a blog post or article on a topic related to the course will help you synthesize your understanding of the material and share your knowledge with others.
Browse courses on Blogging
Show steps
  • Choose a topic that is relevant to the course material
  • Research the topic and gather information from credible sources
  • Write a clear and concise blog post or article
  • Edit and proofread your work
  • Publish your blog post or article on a relevant platform

Career center

Learners who complete Machine Learning in the Enterprise - 日本語版 will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy ML models. They work closely with Data Scientists to ensure that ML models are accurate and reliable. This course is a great fit for aspiring Machine Learning Engineers as it provides a comprehensive overview of the ML workflow, as well as hands-on experience with all aspects of ML model development. The course also covers topics such as Docker and Kubernetes, which are essential for deploying ML models in production.
Data Scientist
Data Scientists analyze large sets of data in order to find patterns and trends. They use their findings to help companies make better decisions. This course may be useful for aspiring Data Scientists as it provides an overview of the ML workflow, as well as hands-on experience with data preprocessing, model training, and model evaluation. The course also covers topics such as hyperparameter tuning and model monitoring, which are essential for building and deploying successful ML models.
Data Analyst
Data Analysts clean, analyze, and interpret data in order to help companies make better decisions. This course may be useful for aspiring Data Analysts as it provides an overview of the ML workflow, as well as hands-on experience with data preprocessing and data visualization. The course also covers topics such as SQL and Python, which are essential for working with data.
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They work with a variety of stakeholders, including engineers, managers, and executives, to improve efficiency and productivity. This course may be useful for aspiring Operations Research Analysts as it provides an overview of the ML workflow, as well as hands-on experience with data analysis and optimization. The course also covers topics such as linear programming and simulation, which are essential for Operations Research Analysts.
Data Architect
Data Architects design and build data systems that meet the needs of organizations. They work with a variety of stakeholders, including data scientists, database administrators, and business analysts, to ensure that data systems are scalable, reliable, and secure. This course may be useful for aspiring Data Architects as it provides an overview of the ML workflow, as well as hands-on experience with data modeling and data governance. The course also covers topics such as cloud computing and big data, which are essential for Data Architects.
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course may be useful for aspiring Software Engineers as it provides an overview of the ML workflow, as well as hands-on experience with software development and testing. The course also covers topics such as Agile development and DevOps, which are essential for modern software development.
Database Administrator
Database Administrators manage and maintain databases. They work with a variety of stakeholders, including developers, database users, and system administrators, to ensure that databases are available, reliable, and secure. This course may be useful for aspiring Database Administrators as it provides an overview of the ML workflow, as well as hands-on experience with database management and administration. The course also covers topics such as data backup and recovery, which are essential for Database Administrators.
Network Administrator
Network Administrators manage and maintain computer networks. They work with a variety of stakeholders, including users, developers, and system administrators, to ensure that networks are available, reliable, and secure. This course may be useful for aspiring Network Administrators as it provides an overview of the ML workflow, as well as hands-on experience with network administration. The course also covers topics such as network security and network troubleshooting, which are essential for Network Administrators.
Product Manager
Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders, including engineers, designers, and marketers, to ensure that products meet the needs of users. This course may be useful for aspiring Product Managers as it provides an overview of the ML workflow, as well as hands-on experience with product development and management. The course also covers topics such as user research and market analysis, which are essential for Product Managers.
Cloud Architect
Cloud Architects design and build cloud computing solutions. They work with a variety of stakeholders, including developers, architects, and business leaders, to ensure that cloud solutions are scalable, reliable, and secure. This course may be useful for aspiring Cloud Architects as it provides an overview of the ML workflow, as well as hands-on experience with cloud computing. The course also covers topics such as cloud security and cloud cost optimization, which are essential for Cloud Architects.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work with a variety of stakeholders, including traders, portfolio managers, and risk managers, to make investment decisions. This course may be useful for aspiring Quantitative Analysts as it provides an overview of the ML workflow, as well as hands-on experience with data analysis and modeling. The course also covers topics such as time series analysis and risk management, which are essential for Quantitative Analysts.
Marketing Manager
Marketing Managers are responsible for developing and implementing marketing campaigns. They work with a variety of stakeholders, including sales, product development, and customer service, to ensure that marketing campaigns are effective. This course may be useful for aspiring Marketing Managers as it provides an overview of the ML workflow, as well as hands-on experience with marketing data analysis and campaign management. The course also covers topics such as digital marketing and social media marketing, which are essential for modern Marketing Managers.
Systems Administrator
Systems Administrators manage and maintain computer systems. They work with a variety of stakeholders, including users, developers, and network administrators, to ensure that systems are available, reliable, and secure. This course may be useful for aspiring Systems Administrators as it provides an overview of the ML workflow, as well as hands-on experience with system administration. The course also covers topics such as cloud computing and virtualization, which are essential for Systems Administrators.
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. They work with a variety of stakeholders, including customers, partners, and other sales professionals, to ensure that sales goals are met. This course may be useful for aspiring Sales Managers as it provides an overview of the ML workflow, as well as hands-on experience with customer relationship management and sales forecasting. The course also covers topics such as negotiation and persuasion, which are essential for Sales Managers.
Business Analyst
Business Analysts help companies improve their operations by identifying and solving problems. They use data analysis to identify inefficiencies and develop solutions. This course may be useful for aspiring Business Analysts as it provides an overview of the ML workflow, as well as hands-on experience with data analysis and problem solving. The course also covers topics such as communication and presentation skills, which are essential for Business Analysts.

Reading list

We've selected eight 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 Machine Learning in the Enterprise - 日本語版.
Provides a comprehensive overview of the principles and practices of reinforcement learning. It good reference book for learners who want to delve deeper into the theoretical foundations of reinforcement learning.
Provides a comprehensive overview of the principles and practices of deep learning. It good reference book for learners who want to delve deeper into the theoretical foundations of deep learning.
Provides a comprehensive overview of the principles and practices of statistical pattern recognition and machine learning. It good reference book for learners who want to delve deeper into the theoretical foundations of machine learning.
Provides a comprehensive overview of the principles, practices, and tools for using deep learning for natural language processing (NLP) tasks. It covers topics such as text classification, sentiment analysis, machine translation, and question answering.
Provides a practical introduction to machine learning for hackers and data scientists. It covers topics such as data preprocessing, model selection, model evaluation, and model deployment.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers topics such as data preprocessing, model selection, model evaluation, and model deployment.
Provides a practical introduction to machine learning using Python. It covers topics such as data preprocessing, model selection, model evaluation, and model deployment.

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