Introduction to AI and Machine Learning on GC

What's inside

Syllabus

はじめに

このモジュールでは、学習者が Google Cloud で AI 開発ツールを操作できるようになるというコース目標について説明します。また、Google Cloud 上の 3 レイヤの AI フレームワークに基づくコースの構成についても概要を示します。

AI の基盤

このモジュールでは、コンピューティングやストレージなどのクラウドインフラストラクチャを含む AI の基盤に焦点を合わせます。また、Google Cloud 上の主要なデータと AI 開発プロダクトについても説明します。最後に、BigQuery ML を使用して ML モデルを構築する方法を紹介します。これは、データから AI への移行に役立ちます。

AI 開発オプション

このモジュールでは、Google Cloud で ML プロジェクトを開発するためのさまざまなオプションについて説明します。オプションとして、事前トレーニング済み API などのすぐに使える AI ソリューションから、AutoML などのノーコードおよびローコードソリューション、カスタムトレーニングなどのコードベースのソリューションまで扱います。また、各オプションのメリットとデメリットを比較し、適切な開発ツールを判断できます。

AI 開発ワークフロー

このモジュールでは、データの準備からモデル開発、Vertex AI でのモデルサービングに至るまで、ML ワークフローについて説明します。また、Vertex AI Pipelines を使用してワークフローを自動パイプラインに変換する方法も紹介します。

生成 AI

このモジュールでは、AI の中で特に進化が著しい生成 AI と、その基盤となるテクノロジーである大規模言語モデル（LLM）を紹介します。また、Generative AI Studio や Model Garden など、Google Cloud 上のさまざまな生成 AI 開発ツールについても説明します。最後に、AI ソリューションと組み込みの生成 AI 機能について検討します。

概要

このモジュールでは、最も重要なコンセプト、ツール、テクノロジー、プロダクトについて取り上げ、コース全体の概要を説明します。

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

対象者を明示的に述べている。

データサイエンティスト、AI開発者、MLエンジニアなど、さまざまなスキルを持ったユーザーを対象としている。

業界標準のGoogle Cloud AI基盤の概念を学習できる。

事前トレーニング済みAPI、ノーコードおよびローコードソリューション、コードベースのソリューションなど、多様なAI開発オプションを提供している。

データの準備からモデル開発、モデルの提供まで、実践的なMLワークフローを学ぶことができる。

大規模言語モデルと生成AIのコンセプトを学ぶことができる。

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 Introduction to AI and Machine Learning on GC - 日本語版 with these activities:

復習: 機械学習の基礎

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この活動により、過去のコースワークや教材を見直して練習問題に取り組むことで、コース開始前に機械学習の基礎知識を復習できます。

Browse courses on ML

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前回のコースワークや教材を確認する
練習問題を解く

Create a resource compilation

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Gather a collection of useful links, articles, and tutorials for easy referencing while working on ML projects.

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Search for resources related to topics like AI fundamentals, ML algorithms, and Cloud ML tools.
Organize resources into categories like 'tutorials', 'documentation', and 'best practices'.
Consider using tools like Google Keep or Notion to keep your compilation organized.

Complete Google Cloud ML tutorials

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Hands-on tutorials provide a structured environment to practice ML concepts and become familiar with Google Cloud ML tools.

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Identify Google Cloud ML tutorials that align with your interests and skill level.
Follow the tutorial instructions carefully, completing all exercises and code examples.
Experiment with different parameters and options to observe their impact on ML models.

Five other activities

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Join an ML study group

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Engaging with peers in a study group facilitates knowledge sharing, collaborative learning, and motivation.

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Find or create an ML study group with other learners or colleagues.
Meet regularly to discuss course material, share insights, and work on projects together.
Take turns leading discussions and presenting findings to enhance understanding.

Solve ML coding challenges

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Regular practice with ML coding challenges helps strengthen your problem-solving abilities and coding proficiency.

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Find ML coding challenges on platforms like LeetCode or Kaggle.
Attempt to solve the challenges independently, focusing on understanding the problem and developing an efficient solution.
Review solutions and compare your approach with others to identify areas for improvement.

Attend ML meetups or conferences

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Networking events provide opportunities to connect with industry professionals, learn about the latest ML trends, and stay informed on research advancements.

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Find upcoming ML meetups or conferences in your area or online.
Attend the events and actively engage with speakers and attendees.
Exchange ideas, share experiences, and expand your professional network.

Build an ML project

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Applying your skills to a real-world ML project solidifies your understanding and provides valuable experience.

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Define the problem you want to solve and gather the necessary data.
Choose appropriate ML algorithms and train models using Google Cloud ML tools.
Evaluate the performance of your models and make necessary adjustments.
Deploy your ML model and monitor its performance in a production environment.

Contribute to open-source ML projects

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Engaging with open-source projects allows you to learn from others, contribute to the ML community, and gain practical experience.

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Identify open-source ML projects that align with your interests.
Review the project documentation and codebase to understand its purpose and functionality.
Make contributions to the project, such as bug fixes, feature enhancements, or documentation improvements.

Career center

Learners who complete Introduction to AI and Machine Learning on GC - 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 turn ML models into real-world applications. This course provides a comprehensive overview of the Google Cloud AI and ML services that Machine Learning Engineers can use to build and deploy ML solutions. It covers topics such as data preparation, model development, model deployment, and model monitoring. By taking this course, Machine Learning Engineers can gain the skills and knowledge they need to succeed in their roles.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use AI and ML to solve real-world problems. They analyze data, build models, and develop algorithms to automate tasks and make predictions. This course introduces the Google Cloud AI and ML services that Data Scientists can use to build and deploy AI solutions. It covers topics such as data preparation, model development, and model serving. By taking this course, Data Scientists can gain the skills and knowledge they need to succeed in their roles.

See salaries and explore the career path for Data Scientist

AI Engineer

AI Engineers design, develop, and deploy AI systems. They work on a wide range of projects, from developing self-driving cars to building AI-powered chatbots. This course provides a comprehensive overview of the Google Cloud AI and ML services that AI Engineers can use to build and deploy AI solutions. It covers topics such as data preparation, model development, model deployment, and model monitoring. By taking this course, AI Engineers can gain the skills and knowledge they need to succeed in their roles.

See salaries and explore the career path for AI Engineer

Data Analyst

Data Analysts use data to solve business problems. They analyze data, identify trends, and develop insights that can help businesses make better decisions. This course introduces the Google Cloud AI and ML services that Data Analysts can use to analyze data and develop insights. It covers topics such as data exploration, data visualization, and data mining. By taking this course, Data Analysts can gain the skills and knowledge they need to succeed in their roles.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data and technology to solve business problems. They work with stakeholders to understand business needs, develop solutions, and measure the impact of those solutions. This course introduces the Google Cloud AI and ML services that Business Analysts can use to analyze data and develop insights. It covers topics such as data exploration, data visualization, and data mining. By taking this course, Business Analysts can gain the skills and knowledge they need to succeed in their roles.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course introduces the Google Cloud AI and ML services that Product Managers can use to develop and launch AI-powered products. It covers topics such as data analysis, market research, and product planning. By taking this course, Product Managers can gain the skills and knowledge they need to succeed in their roles.

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Operations Manager

Operations Managers are responsible for the day-to-day operations of a business. They work with a variety of teams, including finance, human resources, and IT, to ensure that the business runs smoothly. This course introduces the Google Cloud AI and ML services that Operations Managers can use to improve the efficiency and effectiveness of their operations. It covers topics such as data analysis, process optimization, and risk management. By taking this course, Operations Managers can gain the skills and knowledge they need to succeed in their roles.

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Quantitative Analyst

Quantitative Analysts use data to develop and implement trading strategies. They work with a variety of financial instruments, including stocks, bonds, and commodities. This course introduces the Google Cloud AI and ML services that Quantitative Analysts can use to develop and implement trading strategies. It covers topics such as data analysis, financial modeling, and risk management. By taking this course, Quantitative Analysts can gain the skills and knowledge they need to succeed in their roles.

See salaries and explore the career path for Quantitative Analyst

Risk Manager

Risk Managers are responsible for identifying and managing risks that could harm a business. They work with a variety of teams, including finance, operations, and compliance, to develop and implement risk management strategies. This course introduces the Google Cloud AI and ML services that Risk Managers can use to identify and manage risks. It covers topics such as data analysis, risk assessment, and risk mitigation. By taking this course, Risk Managers can gain the skills and knowledge they need to succeed in their roles.

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Compliance Officer

Compliance Officers are responsible for ensuring that a business complies with all applicable laws and regulations. They work with a variety of teams, including legal, finance, and operations, to develop and implement compliance programs. This course introduces the Google Cloud AI and ML services that Compliance Officers can use to improve the efficiency and effectiveness of their compliance programs. It covers topics such as data analysis, risk assessment, and compliance monitoring. By taking this course, Compliance Officers can gain the skills and knowledge they need to succeed in their roles.

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Information Security Analyst

Information Security Analysts are responsible for protecting a business's information systems from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with a variety of teams, including IT, operations, and compliance, to develop and implement information security programs. This course introduces the Google Cloud AI and ML services that Information Security Analysts can use to improve the efficiency and effectiveness of their information security programs. It covers topics such as data analysis, risk assessment, and security monitoring. By taking this course, Information Security Analysts can gain the skills and knowledge they need to succeed in their roles.

See salaries and explore the career path for Information Security Analyst

Sales Manager

Sales Managers are responsible for leading sales teams and achieving sales goals. They work with customers to identify their needs and develop solutions that meet those needs. This course introduces the Google Cloud AI and ML services that Sales Managers can use to develop and execute AI-powered sales strategies. It covers topics such as data analysis, market research, and customer relationship management. By taking this course, Sales Managers can gain the skills and knowledge they need to succeed in their roles.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They work on a wide range of projects, from developing mobile apps to building enterprise software. This course provides a comprehensive overview of the Google Cloud AI and ML services that Software Engineers can use to build and deploy AI-powered software applications. It covers topics such as data preparation, model development, model deployment, and model monitoring. By taking this course, Software Engineers can gain the skills and knowledge they need to succeed in their roles.

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Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. They work with a variety of teams, including sales, product, and design, to create and deliver marketing materials that reach target audiences. This course introduces the Google Cloud AI and ML services that Marketing Managers can use to develop and execute AI-powered marketing campaigns. It covers topics such as data analysis, market research, and campaign management. By taking this course, Marketing Managers can gain the skills and knowledge they need to succeed in their roles.

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Financial Analyst

Financial Analysts use data to analyze financial performance and make investment recommendations. They work with a variety of clients, including individuals, businesses, and governments. This course introduces the Google Cloud AI and ML services that Financial Analysts can use to analyze data and make investment recommendations. It covers topics such as data analysis, financial modeling, and risk management. By taking this course, Financial Analysts can gain the skills and knowledge they need to succeed in their roles.

See salaries and explore the career path for Financial Analyst