名古屋のAI for Software Developmentトレーニング

GitHub Copilot is an AI-powered code completion tool that helps accelerate development while improving quality and productivity. Combined with Artificial Intelligence applications in projects, infrastructure, and software, managers can leverage AI to optimize resource allocation, streamline workflows, and enhance decision-making.

This instructor-led, live training (online or onsite) is aimed at advanced-level managers who wish to deepen their knowledge of GitHub Copilot while also exploring practical AI applications in corporate environments, with examples relevant to large-scale projects and industries such as oil and gas.

By the end of this training, participants will be able to:

• Apply advanced Copilot functionalities in large-scale corporate projects.
• Integrate Copilot into multidisciplinary workflows for maximum efficiency.
• Leverage AI tools to optimize project management, infrastructure, and software acquisition.
• Implement AI-based strategies to improve planning, estimation, and time optimization.
• Recognize practical AI applications in industry-specific scenarios such as oil and gas.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises and case studies.
• Live-lab demonstrations of AI tools and Copilot workflows.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Artificial Intelligence (AI) is a set of technologies that simulate human intelligence in machines and applications, enabling smarter and more adaptive software.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to build AI-powered applications using real-world tools and platforms.

By the end of this training, participants will be able to:

• Understand core AI and machine learning concepts.
• Develop AI features using Python and popular libraries.
• Apply AI techniques to real software development projects.
• Evaluate models and deploy intelligent services.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

GitHub Copilotは、AIを活用したコーディングアシスタントで、YAML設定の作成、GitHub Actions、デプロイスクリプトなどのDevOps操作を自動化するのに役立ちます。

このインストラクター主導の実践的なトレーニング（オンラインまたはオンサイト）は、初心者から中級レベルの専門家向けで、GitHub Copilotを使用してDevOpsタスクを効率化し、自動化を改善し、生産性を向上させたい人を対象としています。

このトレーニング終了時には、参加者は以下のことができるようになります：

• GitHub Copilotを使用してシェルスクリプト、設定、CI/CDパイプラインを支援する。
• YAMLファイルとGitHub ActionsでのAIコード補完を利用する。
• テスト、デプロイ、自動化ワークフローを加速する。
• AIの制限と最善の実践方法を理解した上でCopilotを適切に使用する。

コース形式

• インタラクティブな講義とディスカッション。
• 豊富な演習と実践。
• ライブラボ環境での手動実装。

コースのカスタマイズオプション

• このコースのカスタマイズされたトレーニングを希望される場合は、お問い合わせください。

Huawei Ascend is a family of AI processors designed for high-performance inference and training.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI engineers and data scientists who wish to develop and optimize neural network models using Huawei’s Ascend platform and the CANN toolkit.

By the end of this training, participants will be able to:

• Set up and configure the CANN development environment.
• Develop AI applications using MindSpore and CloudMatrix workflows.
• Optimize performance on Ascend NPUs using custom operators and tiling.
• Deploy models to edge or cloud environments.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Huawei Ascend and CANN toolkit in sample applications.
• Guided exercises focused on model building, training, and deployment.

Course Customization Options

• To request a customized training for this course based on your infrastructure or datasets, please contact us to arrange.

This instructor-led, live training in 名古屋 (online or onsite) is aimed at intermediate-level software professionals who wish to leverage AI and machine learning to improve efficiency and innovation in software development.

By the end of this training, participants will be able to:

• Understand the role of AI and machine learning in automating software development tasks.
• Implement AI tools to generate code, tests, and documentation.
• Apply AI techniques for code optimization, quality assurance, and debugging.
• Integrate AI into the DevOps and CI/CD pipelines for improved deployment strategies.
• Address ethical considerations and challenges in AI-augmented software engineering.

This instructor-led, live training in 名古屋 (online or onsite) is aimed at intermediate-level to advanced-level participants who wish to optimize team workflows, enhance collaborative coding practices, and effectively manage Copilot usage in multi-developer environments.

By the end of this training, participants will be able to:

• Set up GitHub Copilot for team environments.
• Utilize Copilot to enhance collaborative coding practices.
• Optimize team workflows using Copilot’s features.
• Manage Copilot’s integration in multi-developer projects.
• Maintain consistent code quality and standards across teams.
• Leverage advanced Copilot features for team-specific needs.
• Combine Copilot with other collaborative tools for efficiency.

This instructor-led, live training in 名古屋 (online or onsite) is aimed at intermediate-level QA engineers, developers, and team leads who wish to leverage GitHub Copilot for more efficient debugging, code quality enhancement, and streamlined code review.

By the end of this training, participants will be able to:

• Set up GitHub Copilot for debugging and code review purposes.
• Use Copilot to identify and resolve bugs efficiently.
• Enhance code quality with AI-assisted suggestions.
• Streamline code review processes with Copilot's capabilities.
• Collaborate effectively using Copilot in team environments.

Course Customization Options

Format of the Course

By the end of this training, participants will be able to:

Prompt Engineering for Software Development is a hands-on course designed to teach developers how to craft effective prompts that optimize the usefulness, precision, and safety of AI-generated outputs in the context of software engineering tasks.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to integrate prompt engineering techniques into their use of AI coding assistants, code analyzers, and intelligent automation tools.

• Create precise prompts for code generation, review, and optimization tasks.
• Use prompt chaining and refinement to improve AI responses in iterative workflows.
• Generate structured outputs such as test cases, SQL queries, and documentation from minimal input.
• Mitigate hallucination and improve control over LLM behavior in technical environments.

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

• To request a customized training for this course, please contact us to arrange.

このインストラクター主導のライブトレーニング（オンラインまたはオンサイト）は、GitHub Copilotを使用して反復的なコーディングタスクを自動化し、UI/UXデザインを改善し、フロントエンドワークフローを効率化したい中級レベルのフロントエンド開発者向けです。

このトレーニング終了時には、参加者は以下のことができるようになります：

• フロントエンド開発プロジェクトでGitHub Copilotをセットアップする。
• GitHub Copilotを利用してHTML、CSS、JavaScriptコードを効率的に生成する。
• AI生成のコード提案を使用してUI/UXデザインプロセスを改善する。
• 実践的なGitHub Copilot統合戦略を利用してフロントエンドワークフローを強化する。
• GitHub Copilotの支援を利用してフロントエンドコードのトラブルシューティングとデバッグを行う。

Responsible AI in Software Development is a foundational course that addresses the ethical, legal, and governance considerations when using AI tools in the software development process. The course emphasizes transparency, fairness, intellectual property concerns, and safe deployment of AI-generated code.

This instructor-led, live training (online or onsite) is aimed at beginner-level technical and non-technical professionals who wish to ensure ethical, compliant, and risk-aware use of AI in their software projects.

By the end of this training, participants will be able to:

• Identify and mitigate ethical and legal risks associated with AI-assisted development.
• Understand bias in AI-generated code and how to evaluate fairness.
• Manage software licenses and attribution related to AI-generated content.
• Establish governance, policy, and compliance frameworks for AI use in development teams.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI for Software Requirements and User Story Generation is a practical course focused on using generative AI to transform stakeholder input into structured requirements, epics, user stories, and acceptance criteria suitable for Agile development workflows.

This instructor-led, live training (online or onsite) is aimed at beginner-level product and project professionals who wish to use tools like ChatGPT or Claude to improve clarity, speed, and traceability in requirement gathering and refinement.

By the end of this training, participants will be able to:

• Use AI prompts to gather and refine business requirements.
• Convert feature requests into well-structured user stories and epics.
• Generate acceptance criteria, edge cases, and definitions of done with AI assistance.
• Collaborate with development teams using AI-structured documentation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Cursor is an AI-powered development environment that enhances productivity and reliability across data and machine learning workflows through intelligent code generation, context-aware suggestions, and streamlined documentation.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data and ML professionals who wish to integrate Cursor into their daily workflows for faster prototyping, scalable pipeline development, and improved model operations.

After completing this training, participants will be able to:

• Use Cursor to accelerate notebook development and code exploration.
• Generate, refactor, and document ETL and feature engineering pipelines.
• Leverage AI-assisted code for model training, tuning, and evaluation.
• Enhance reproducibility, collaboration, and operational consistency in ML workflows.

Format of the Course

• Interactive lectures and demonstrations.
• Practical, hands-on exercises in live coding environments.
• Case studies integrating Cursor with ML pipelines and model ops tools.

Course Customization Options

• This training can be tailored to specific frameworks such as TensorFlow, PyTorch, or scikit-learn, or to organizational MLOps platforms.

このインストラクター主導の実践的なトレーニング（オンラインまたはオンサイト）は、Pythonの初心者から中級レベルの開発者がGitHub Copilotを活用してPython固有のタスク、デバッグ、および機械学習ワークフローを実装することを目指しています。

このトレーニング終了時には、参加者は次のことができるようになります：

• Python開発のためにGitHub Copilotを設定および構成する。
• Copilotを利用して効率的なPythonコードを書く。
• AI生成の提案を使用してPythonアプリケーションをデバッグする。
• 繰り返し作業の自動化とワークフローアップ効率の向上を行う。
• Pythonでの機械学習プロジェクトの実装にCopilotを利用する。

Graphiteは、開発者の生産性とコードレビューを最適化するプラットフォームで、プルリクエストワークフローを改善し、チーム間のコラボレーションを強化し、GitHubやAIベースのコードレビューアシスタントなどのツールと統合できます。

この講師主導のライブトレーニング（オンラインまたはオンサイト）は、中級レベルのソフトウェア開発チームを対象としており、開発ワークフローを合理化し、レビュー効率を向上させ、エンタープライズDevOpsパイプライン内でのGraphiteの有効な導入を目指しています。

このトレーニング終了時には、参加者は以下のことができます：

• エンタープライズスケールの開発環境でGraphiteを展開および構成する。
• プルリクエスト管理を自動化し、コードレビューの効率を向上させる。
• GraphiteをGitHub、CI/CDシステム、OpenAI、Deepseek、LlaMA、Qwen、Mistral SmallなどのAIアシスタントと統合する。
• Graphiteの分析ダッシュボードを使用して開発者の生産性を測定する。

コース形式

• 対話型の講義とディスカッション。
• たくさんの演習と実践。
• ライブラボ環境での手を動かす実装。

コースのカスタマイズオプション

• このコースのカスタマイズトレーニングをご希望の場合、お気軽にお問い合わせください。

Cursor is an advanced AI-powered development environment that allows engineers to extend, fine-tune, and customize its coding intelligence for specialized use cases and enterprise workflows.

This instructor-led, live training (online or onsite) is aimed at advanced-level developers and AI engineers who wish to design tailored prompt systems, fine-tune model behavior, and build custom extensions for internal development automation.

By the conclusion of this training, participants will be able to:

• Design and test advanced prompt templates for precise AI behavior.
• Connect Cursor to internal APIs and knowledge bases for context-aware code generation.
• Develop fine-tuned or domain-adapted AI models for specialized tasks.
• Build and deploy custom tools or adapters that extend Cursor’s functionality securely.

Format of the Course

• Technical presentations and guided demonstrations.
• Hands-on development and prompt optimization labs.
• Practical projects integrating Cursor with real-world enterprise systems.

Course Customization Options

• This course can be customized to align with specific internal architectures, AI frameworks, or security compliance requirements.

この講師主導のライブトレーニング（オンラインまたはオンサイト）は、GitHub Copilot をチームプロジェクトにカスタマイズし、その高度な機能を利用し、CI/CD パイプラインにシームレスに統合してコラボレーションと生産性を向上させたい上級レベルの参加者を対象としています。

このトレーニングの終了時には、参加者は以下のことができます：

• 特定のプロジェクト要件やチームワークフローに GitHub Copilot をカスタマイズします。
• 複雑なコーディングタスクで Copilot の高度な機能を活用します。
• CI/CD パイプラインやコラボレーティブ環境に GitHub Copilot を統合します。
• AI 駆動のツールを使用してチームコラボレーションを最適化します。
• Copilot の設定や権限を効果的に管理し、トラブルシューティングを行います。

Generative AI for Front-End Development explores how large language models and AI-powered tools can accelerate front-end development through prompt-based code generation, layout prototyping, and UI optimization.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level front-end professionals who wish to use generative AI to build and iterate user interfaces more quickly and creatively.

By the end of this training, participants will be able to:

• Use AI tools to generate HTML, CSS, and JavaScript from natural language prompts.
• Create and refine UI components and layouts using generative models.
• Prototype responsive web designs rapidly without writing all code from scratch.
• Optimize front-end code using AI suggestions and automation workflows.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in 名古屋 (online or onsite) is aimed at developers ranging from novices to experts who wish to leverage AI for code generation with Tabnine.

By the end of this training, participants will be able to:

• Understand the basics of AI-powered code generation.
• Install and configure Tabnine in their development environment.
• Utilize Tabnine for efficient code completion and error correction.
• Create and train custom AI models with Tabnine for specialized tasks.

Vibe coding is an emerging paradigm where developers collaborate with large language models to generate, refine, and optimize code through natural language interactions.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and technical leads who wish to master the use of AI coding assistants and open-source LLMs to improve productivity, creativity, and workflow automation.

By the end of this training, participants will be able to:

• Understand the principles and philosophy of vibe coding and its impact on software development.
• Implement LLM-assisted coding environments using tools such as Replit, DeepSeek, LlaMA, Qwen, Mistral, and GitHub Copilot.
• Design and execute prompt engineering strategies for effective AI collaboration.
• Integrate vibe coding workflows into enterprise CI/CD pipelines and governance frameworks.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI-Augmented Test Generation and Quality Engineering explores how artificial intelligence tools and large language models (LLMs) can improve test coverage, speed up test creation, and enhance quality assurance processes.

This instructor-led, live training (online or onsite) is aimed at intermediate-level QA professionals who wish to automate and enhance their testing strategies using AI-driven tools and practices.

By the end of this training, participants will be able to:

• Generate unit, integration, and UI tests using AI tools and prompts.
• Use LLMs for exploratory testing, edge-case discovery, and regression analysis.
• Cluster and prioritize test failures and anomalies using AI-assisted triage.
• Integrate AI-based testing into CI/CD pipelines to improve release confidence.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Cursor is an AI-powered development environment that enhances team collaboration, automates code reviews, and integrates seamlessly into modern CI/CD workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level technical professionals who wish to integrate Cursor into their team environments to improve collaboration, streamline reviews, and maintain quality across automated pipelines.

Upon completing this training, participants will be able to:

• Set up and manage team environments in Cursor for collaborative development.
• Leverage AI tools for automated code reviews, pull request generation, and merge validation.
• Implement code governance, review policies, and security guardrails using Cursor’s capabilities.
• Integrate Cursor with CI/CD systems to ensure continuous delivery and consistent quality standards.

Format of the Course

• Instructor-led presentations and team-based discussions.
• Hands-on labs using real-world team collaboration scenarios.
• Live integration exercises with CI/CD and version control tools.

Course Customization Options

• The course can be adapted to specific CI/CD platforms, repository tools, or enterprise security requirements.

This instructor-led, live training in 名古屋 (online or onsite) is aimed at beginner-level to intermediate-level programmers and developers who wish to leverage DeepSeek Coder to enhance coding efficiency and productivity.

By the end of this training, participants will be able to:

• Understand the capabilities and limitations of DeepSeek Coder.
• Generate high-quality code snippets using AI assistance.
• Utilize DeepSeek Coder for debugging and optimizing code.
• Automate repetitive programming tasks using AI tools.

This instructor-led, live training in 名古屋 (online or onsite) is aimed at intermediate-level participants who wish to leverage GitHub Copilot to handle advanced coding use cases, improve productivity, and integrate Copilot into their development workflows.

By the end of this training, participants will be able to:

• Optimize their use of GitHub Copilot for advanced coding tasks.
• Write more efficient, error-free, and maintainable code with Copilot suggestions.
• Integrate GitHub Copilot into their preferred IDEs and workflows.
• Utilize Copilot for debugging and code refactoring.
• Understand the limitations and ethical considerations of using AI-powered coding tools.

This instructor-led, live training in 名古屋 (online or onsite) is aimed at advanced-level developers and team leads who wish to master advanced features of Tabnine.

By the end of this training, participants will be able to:

• Implement Tabnine in complex software projects.
• Customize and train Tabnine's AI models for specific use cases.
• Integrate Tabnine into team workflows and development pipelines.
• Enhance code quality and accelerate development cycles using Tabnine's insights.

This instructor-led, live training in 名古屋 (online or onsite) is aimed at beginner-level to intermediate-level software developers who wish to integrate AI coding assistants into their development workflow.

By the end of this training, participants will be able to:

• Understand the role and capabilities of AI coding assistants in software development.
• Utilize various AI coding assistant tools to automate routine coding tasks.
• Integrate AI coding assistants into their software development lifecycle.
• Enhance their productivity and focus on more complex and creative programming tasks.
• Address ethical considerations and responsible use of AI in software development.

Devstralは、コードベース、開発者ツール、APIと連携してエンジニアリング生産性を向上させるコーディングエージェントの構築と実行のために設計されたオープンソースフレームワークです。

この講師主導のライブトレーニング（オンラインまたはオンサイト）は、中級から上級レベルのMLエンジニア、開発者ツールチーム、およびSRE向けに提供され、Devstralを使用してコーディングエージェントを設計、実装、最適化する方法を学びます。

このトレーニング終了時には、参加者は以下のことができるようになります：

• コーディングエージェント開発のためのDevstralをセットアップし、設定する。
• コードベースの探索と変更のためのエージェンシーワークフローを設計する。
• コーディングエージェントを開発者ツールやAPIと統合する。
• セキュアで効率的なエージェントの展開に関するベストプラクティスを実装する。

コース形式

• 対話型講義とディスカッション。
• 多くの演習と練習。
• ライブラボ環境での手動実装。

コースのカスタマイズオプション

• このコースのカスタマイズされたトレーニングをリクエストする場合は、ご連絡ください。

Cursor is an AI-powered code editor designed to enhance developer productivity through intelligent code completions, contextual edits, and adaptive assistance.

This instructor-led, live training (online or onsite) is aimed at beginner-level developers and engineering teams who wish to streamline their coding workflow and safely leverage AI suggestions for improved efficiency.

Upon completion of this training, participants will be able to:

• Install and configure Cursor for optimal use in development projects.
• Understand and apply AI-assisted code completion, in-editor chat, and refactoring tools.
• Evaluate, accept, or modify AI-generated code suggestions effectively and securely.
• Adopt best practices for team onboarding, collaboration, and version control integration.

Format of the Course

• Interactive lecture and discussion.
• Hands-on demonstrations and guided exercises.
• Real-world coding challenges and lab practice using Cursor.

Course Customization Options

• This course can be tailored to specific programming languages or frameworks used by your team.

この講師主導のライブトレーニング（オンラインまたはオンサイト）は、初心者の開発者向けに設計されており、GitHub Copilotの機能を理解し、セットアップを行い、効果的に使用してコーディング体験を向上させる方法を学びます。

このトレーニングの終了時には、参加者は以下のことができるようになります：

• GitHub Copilotとは何か、どのように動作するのかを理解します。
• 対応するコードエディターでGitHub Copilotをセットアップします。
• AIの支援を利用してコードを迅速に書く、リファクタリングし、デバッグします。
• GitHub Copilotを使用してコーディング技術や解決策を探求します。
• 日常のワークフローにGitHub Copilotを統合するためのベストプラクティスを適用します。

LLMs for Code Understanding, Refactoring, and Documentation is a technical course focused on applying large language models (LLMs) to improve code quality, reduce technical debt, and automate documentation tasks across software teams.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level software professionals who wish to use LLMs such as GPT to analyze, refactor, and document complex or legacy codebases more effectively.

By the end of this training, participants will be able to:

• Use LLMs to explain code, dependencies, and logic in unfamiliar repositories.
• Identify and refactor anti-patterns and improve code readability.
• Automatically generate and maintain in-line comments, README files, and API documentation.
• Integrate LLM-driven insights into existing CI/CD and review workflows.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

このインストラクター主導のライブトレーニング（オンラインまたはオンサイト）は、中級レベルの Python 開発者やデータサイエンティストが生産性を向上させるために Tabnine を活用したい方を対象としています。

このトレーニング終了時には、参加者は以下が able になります：

• Python 開発環境に Tabnine をインストールおよび設定します。
• Tabnine の自動補完機能を使用して Python コードをより効率的に書きます。
• コーディングスタイルやプロジェクトの要件に合わせて Tabnine の動作をカスタマイズします。
• Python コードに対して Tabnine の AI モデルがどのように機能するかを理解します。

This instructor-led, live training in 名古屋 (online or onsite) is aimed at beginner-level participants who wish to understand AI coding assistants, their capabilities, how they can enhance the development process, and the ethical considerations surrounding their use.

By the end of this training, participants will be able to:

• Understand the purpose and capabilities of AI coding assistants.
• Identify different AI coding assistant tools and their features.
• Utilize AI coding assistants for basic coding tasks.
• Address basic ethical considerations and responsible use of AI in development.

AI-Driven Software Development Lifecycle (SDLC) is a practical course that explores how artificial intelligence can enhance each phase of the software development process. From automating requirement analysis to intelligent test generation and deployment optimization, participants will learn how to integrate AI tools and techniques across the full lifecycle.

This instructor-led, live training (online or onsite) is aimed at intermediate-level software leaders who wish to modernize their SDLC with AI-assisted workflows and tools.

By the end of this training, participants will be able to:

• Leverage AI for converting business input into structured requirements and user stories.
• Use LLMs to improve code documentation, review, and refactoring.
• Automate test case generation and coverage analysis using AI tools.
• Apply AI-driven monitoring and deployment decision-making in CI/CD pipelines.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training (online or onsite) is aimed at intermediate-level software developers who wish to boost productivity and code quality using AI-assisted coding with Cursor.

By the end of this training, participants will be able to:

• Install and configure Cursor for AI-assisted software development.
• Integrate Cursor with Git repositories and development workflows.
• Use natural language to generate, debug, and optimize code.
• Leverage AI capabilities for refactoring, documentation, and testing.

This instructor-led, live training in 名古屋 (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to learn how to utilize the capabilities of GitHub Copilot effectively within modern development workflows.

このインストラクター主導のライブトレーニング（オンラインまたはオンサイト）は、初級レベルの開発者がTabnineを使用してコーディング効率を向上させたいと考えている人向けです。

このトレーニング終了時には、参加者は以下のことができるようになります：

• お好みのIDEにTabnineをインストールし、セットアップする。
• Tabnineのオートコンプリート機能を使用してコーディングを加速する。
• 最適な支援のためにTabnineの設定をカスタマイズする。
• TabnineのAIがコードから学習し、より良い提案を行う方法を理解する。

This immersive, one-day workshop that empowers software teams to integrate cutting-edge AI tools throughout the Software Development Lifecycle (SDLC). Structured as an interactive game with progressive “levels,” this experience blends technical skill-building, creativity, and collaboration.

Participants will tackle real-world challenges across all stages of the SDLC — from requirement gathering and design to coding, testing, deployment, and monitoring — using AI-powered tools like ChatGPT, GitHub Copilot, and ML-based log analysis systems.

Designed for modern development environments, this workshop fosters AI fluency, team synergy, and a deep understanding of how to augment software delivery pipelines with generative AI and machine learning capabilities.