AI-Driven Software Development Lifecycle (SDLC) Training Course

This advanced training on Taiwan focuses on GitHub Copilot and AI applications for corporate infrastructure. It provides managers with the skills to optimize project management, streamline operations, and improve decision-making through AI tools, with a specific emphasis on large-scale projects.

This instructor-led training on Taiwan enables advanced developers to master prompt engineering, fine-tuning, and custom tooling within Cursor. Participants will learn to design precise AI behaviors, integrate internal knowledge bases, and build secure, domain-adapted extensions for enterprise workflows.

This instructor-led, live training in Taiwan (available online or onsite) is targeted at advanced-level participants who aim to customize GitHub Copilot for team projects, leverage its advanced features, and seamlessly integrate it into CI/CD pipelines to enhance collaboration and productivity.

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

• Customize GitHub Copilot for specific project needs and team workflows.
• Leverage advanced features of Copilot for complex coding tasks.
• Integrate GitHub Copilot into CI/CD pipelines and collaborative environments.
• Optimize team collaboration using AI-powered tools.
• Manage and troubleshoot Copilot settings and permissions effectively.

This instructor-led live training in Taiwan (online or onsite) is aimed at developers who wish to use GitHub Copilot Agent Mode to autonomously build features, run tests, and manage larger coding tasks.

By the end of this training, participants will be able to activate Agent Mode, plan and iterate within the agent loop, execute terminal commands, and implement enterprise governance.

This instructor-led, live training session (available online or onsite) is tailored for intermediate-level software developers aiming to improve productivity and code quality through AI-assisted coding with Cursor.

By the end of the course, participants will be capable of:

• Installing and configuring Cursor for AI-enhanced development.
• Integrating Cursor with Git repositories and development workflows.
• Using natural language to generate, debug, and optimize code.
• Leveraging AI features for refactoring, documentation, and testing.

This instructor-led training on Taiwan presents Cursor as an AI-enhanced environment designed to accelerate data and ML workflows. Learners will master the techniques to optimize notebook development, ETL pipelines, and model operations through hands-on exercises that improve productivity and ensure operational consistency.

This instructor-led training in Taiwan assists developers in adopting Cursor, an AI-powered editor that enhances productivity through intelligent completions and contextual editing. Participants will develop hands-on proficiency in refactoring, secure review of AI suggestions, and team collaboration to optimize workflows.

This live, instructor-led training in Taiwan empowers teams to integrate Cursor for streamlined collaboration and code reviews. Participants will learn to manage workspaces, automate AI-assisted pull requests, enforce governance policies, and link Cursor to CI/CD pipelines to ensure consistent quality.

This instructor-led, live training in Taiwan (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.

This instructor-led, live training in Taiwan (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 session in Taiwan (available online or in-person) is designed for beginner-level developers who want to boost their coding efficiency with the help of Tabnine.

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

• Install and set up Tabnine in their preferred IDE.
• Utilize Tabnine's autocomplete features to speed up coding.
• Customize Tabnine's settings for optimal assistance.
• Understand how Tabnine's AI learns from their code to provide better suggestions.

This instructor-led, live training in Taiwan (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 Taiwan (online or on-site) 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.

This instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate-level Python developers and data scientists who wish to boost their productivity with the help of Tabnine.

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

• Install and configure Tabnine in their Python development environment.
• Use Tabnine's autocomplete features to write Python code more efficiently.
• Customize Tabnine's behavior to fit their coding style and project needs.
• Understand how Tabnine's AI model works specifically with Python code.