Machine Learning in the Enterprise - 日本語版 from Coursera

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。

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。

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

Know what's good

, what to watch for

, and possible dealbreakers

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。

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

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Setting up a Cloud environment will help you familiarize yourself with the tools and services you'll be using throughout the course.

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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

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Participating in a peer discussion forum will allow you to connect with other students and get feedback on your work.

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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

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Practicing data preparation tasks will help you develop the skills you need to prepare your data for modeling.

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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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Complete the Coursera tutorial on AutoML Tables

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Completing the Coursera tutorial on AutoML Tables will give you hands-on experience with using AutoML to train and deploy machine learning models.

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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

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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.

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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

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Mentoring a junior student in the course will help you solidify your understanding of the material and develop your leadership skills.

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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

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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.

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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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Data Architect

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.

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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.

See salaries and explore the career path for Database Administrator

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.

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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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Cloud Architect

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.

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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.

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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.

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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.

See salaries and explore the career path for Sales Manager

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.

See salaries and explore the career path for Business Analyst

Machine Learning in the Enterprise - 日本語版

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