AI-Augmented Test Generation and Quality Engineering Training Course

This advanced training in Latvia explores GitHub Copilot and AI applications for corporate infrastructure. It empowers managers to master skills for optimizing project management, streamlining workflows, and making informed decisions using AI tools, with a specific emphasis on large-scale projects.

This instructor-led training in Latvia 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 to support enterprise workflows.

This instructor-led, live training conducted in Latvia (either online or on-site) targets advanced-level participants who aim to customize GitHub Copilot for team projects, leverage its advanced functionalities, and integrate it into CI/CD pipelines to foster better collaboration and productivity.

Following this training, participants will possess the ability to:

• Customize GitHub Copilot to suit specific project needs and team workflows.
• Apply Copilot's advanced features to resolve complex coding challenges.
• Integrate GitHub Copilot into CI/CD pipelines and collaborative settings.
• Improve team collaboration through the use of AI-driven tools.
• Manage and troubleshoot Copilot configurations and permissions with efficiency.

This instructor-led, live training in Latvia (delivered online or on-site) is tailored for developers aiming to utilize GitHub Copilot Agent Mode for autonomous feature development, automated testing, and the management of large-scale coding tasks.

By the end of the session, participants will be able to activate Agent Mode, execute iterative planning and coding within the agent loop, run terminal commands, and enforce enterprise governance standards.

This instructor-led live session (online or onsite) is tailored for intermediate software developers looking to enhance their productivity and code quality by leveraging Cursor’s AI-assisted coding features.

By the conclusion of the training, attendees will be able to:

• Install and configure Cursor for AI-supported development tasks.
• Integrate Cursor into Git repositories and standard development processes.
• Use natural language prompts to generate, troubleshoot, and refine code.
• Harness AI features for refactoring, generating documentation, and executing tests.

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

This instructor-led training in Latvia assists developers in adopting Cursor, an AI-enabled editor that enhances productivity through intelligent completions and context-specific edits. Learners develop practical skills in refactoring, secure review of AI suggestions, and team collaboration to refine their workflows.

This live, instructor-led training in Latvia supports teams in integrating Cursor to optimize collaboration and code reviews. Learn to manage workspaces, automate AI-assisted PRs, enforce governance policies, and link Cursor to CI/CD pipelines to ensure consistent quality.

This live, instructor-led training in Latvia (delivered online or onsite) targets developers from beginner to intermediate levels, focusing on mastering the capabilities of GitHub Copilot within modern development workflows.

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

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

• Configure GitHub Copilot for team environments.
• Utilize Copilot to enhance collaborative coding practices.
• Streamline team workflows by leveraging Copilot’s features.
• Oversee the integration of Copilot within multi-developer projects.
• Maintain consistent code quality and standards across teams.
• Leverage advanced Copilot capabilities to address team-specific needs.
• Integrate Copilot with other collaborative tools to increase efficiency.

Offered as an instructor-led, live training session either online or on-site, this course is tailored for entry-level developers seeking to enhance their coding efficiency through Tabnine.

Upon completion, participants will be equipped to:

• Install and configure Tabnine within their preferred Integrated Development Environment (IDE).
• Leverage Tabnine’s autocomplete capabilities to accelerate coding workflows.
• Adjust Tabnine’s settings to optimize assistance.
• Comprehend how Tabnine’s AI engine learns from user code to refine suggestions.

This live, instructor-led training in Latvia (delivered online or onsite) is designed for expert developers and team leaders aiming to perfect the advanced functionalities of Tabnine.

By the conclusion of this program, participants will be capable of:

• Deploying Tabnine in complex software environments.
• Adapting and training Tabnine's AI models for specialized applications.
• Integrating Tabnine into team operations and development pipelines.
• Improving code standards and speeding up development phases via Tabnine's insights.

This live, instructor-led training session, conducted in Latvia (either online or on-site), is designed for developers from novice to expert level who aim to utilize AI for code generation through Tabnine.

By the conclusion of this session, attendees will be able to:

• Comprehend the essentials of AI-driven code generation.
• Install and set up Tabnine within their development stack.
• Employ Tabnine for streamlined code completion and bug fixing.
• Build and train dedicated AI models using Tabnine for niche tasks.

This instructor-led live training, conducted Latvia (either online or on-site), is intended for intermediate-level Python developers and data scientists aiming to improve their productivity with the assistance of Tabnine.

By the conclusion of this training, participants will have the ability to:

• Install and configure Tabnine within their Python development environment.
• Utilize Tabnine's autocomplete features to enhance the efficiency of writing Python code.
• Customize Tabnine's behavior to better suit their coding style and project needs.
• Comprehend how Tabnine's AI model operates specifically with Python code.