Building Coding Agents with Devstral: From Agent Design to Tooling Training Course

This advanced Uzbekistan course covers the integration of GitHub Copilot and AI applications within corporate infrastructure. It provides managers with the necessary skills to refine project oversight, simplify workflows, and elevate decision-making through AI tools, with a specific emphasis on large-scale operations.

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

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at advanced-level participants who wish to customize GitHub Copilot for team projects, utilize its advanced features, and integrate it seamlessly into CI/CD pipelines for enhanced 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 Uzbekistan (online or onsite) is targeted at developers who intend to use GitHub Copilot's Agent Mode to autonomously build features, execute tests, and handle 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-level governance.

This live training, led by an instructor and available online or onsite, is designed for intermediate software developers seeking to enhance productivity and code quality through AI-assisted coding with Cursor.

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

• Install and set up Cursor for AI-assisted software development.
• Connect Cursor with Git repositories and integrate it into development workflows.
• Utilize natural language to generate, debug, and optimize code.
• Apply AI features for refactoring, documentation creation, and testing.

This instructor-led training in Uzbekistan introduces Cursor as an AI-powered environment to accelerate data and ML workflows. Participants will learn to streamline notebook development, ETL pipelines, and model operations through practical exercises that enhance productivity and operational consistency.

This instructor-led training on Uzbekistan guides developers in adopting Cursor, an AI-powered editor that enhances productivity through smart completions and contextual edits. Learners acquire practical skills in refactoring, secure review of AI suggestions, and team collaboration to refine their workflows.

This live, instructor-led training in Uzbekistan supports teams in integrating Cursor to optimize collaboration and code review workflows. Participants will learn to manage workspaces, automate AI-assisted pull requests, enforce governance standards, and link Cursor to CI/CD pipelines to ensure consistent quality.

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

This instructor-led live training in Uzbekistan (online or on-site) is designed for intermediate to advanced participants seeking to optimize team workflows, enhance collaborative coding practices, and manage Copilot usage effectively in multi-developer environments.

Upon completing this training, participants will be able to:

• Configure GitHub Copilot for team environments.
• Apply Copilot to strengthen collaborative coding practices.
• Streamline team workflows using Copilot’s capabilities.
• Oversee Copilot’s integration within multi-developer projects.
• Ensure consistent code quality and standards across all teams.
• Utilize advanced Copilot features tailored to specific team requirements.
• Integrate Copilot with other collaborative tools to boost efficiency.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for beginner developers who want to enhance their coding efficiency with Tabnine.

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

• Install and set up Tabnine in their preferred IDE.
• Use Tabnine's autocomplete features to accelerate coding.
• Adjust Tabnine's settings for maximum assistance.
• Understand how Tabnine's AI learns from their code to offer better suggestions.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for advanced-level developers and team leads who aim to master the advanced capabilities of Tabnine.

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

• Implement Tabnine within 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 by leveraging Tabnine's insights.

This instructor-led, live training in Uzbekistan (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.

This instructor-led, live training in Uzbekistan (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.