AI-Augmented Software Engineering (AIASE) Training Course

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

This instructor-led training on Germany empowers 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 construct secure, domain-adapted extensions for enterprise workflows.

This instructor-led live training, delivered in Germany (either online or onsite), targets advanced participants aiming to customize GitHub Copilot for team projects, exploit its advanced functionalities, and integrate it smoothly into CI/CD pipelines to elevate collaboration and productivity.

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

• Customizing GitHub Copilot to fit specific project needs and team workflows.
• Applying Copilot’s advanced features to handle complex coding tasks.
• Embedding GitHub Copilot into CI/CD pipelines and collaborative environments.
• Improving team collaboration by leveraging AI-powered tools.
• Effectively managing and troubleshooting Copilot configurations and permissions.

This instructor-led, live training in Germany (delivered online or onsite) is tailored for developers seeking to utilize GitHub Copilot Agent Mode for autonomous feature development, automated testing, and the management of complex coding tasks.

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

This live, instructor-led training, offered online or onsite, targets intermediate software developers seeking to improve their efficiency and code quality by leveraging AI-assisted coding with Cursor.

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

• Installing and setting up Cursor for AI-driven development tasks.
• Integrating Cursor with Git repositories and existing workflows.
• Using natural language to create, debug, and optimize code.
• Utilizing AI capabilities for refactoring, documentation, and testing.

This instructor-led training in Germany presents Cursor as an AI-enhanced environment to accelerate data and ML workflows. Learners will master techniques to streamline notebook development, ETL pipelines, and model operations through practical exercises that drive productivity and operational consistency.

This instructor-led session on Germany guides developers in adopting Cursor, an AI-driven editor that enhances productivity through smart completions and contextual edits. Learners will develop practical skills in refactoring, safely reviewing AI suggestions, and fostering team collaboration to optimize their workflows.

This instructor-led, live training in Germany assists teams in integrating Cursor to streamline collaboration and code reviews. Participants will learn to manage workspaces, automate AI-assisted PRs, enforce governance policies, and connect Cursor to CI/CD pipelines to ensure consistent quality.

This live, instructor-led training in Germany (online or onsite) is designed for developers ranging from beginner to intermediate levels who want to effectively leverage GitHub Copilot’s capabilities within modern development processes.

This live, instructor-led training delivered in Germany (either online or onsite) is tailored for intermediate to advanced participants aiming to refine team workflows, improve collaborative coding techniques, and effectively govern Copilot usage in multi-developer contexts.

Upon completion, participants will be equipped to:

• Configure GitHub Copilot for collaborative team environments.
• Leverage Copilot to strengthen collaborative coding methodologies.
• Streamline team workflows by utilizing specific Copilot capabilities.
• Oversee Copilot integration within multi-developer project structures.
• Ensure uniform code quality and adherence to standards across all team members.
• Apply advanced Copilot features to address unique team requirements.
• Integrate Copilot with other collaboration platforms to maximize operational efficiency.

This live, instructor-led training in Germany (delivered online or on-site) is designed for junior developers seeking to boost their coding efficiency through the use of Tabnine.

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

• Install and configure Tabnine within their preferred IDE.
• Leverage Tabnine’s autocomplete capabilities to streamline the coding process.
• Adjust Tabnine’s settings to achieve optimal support.
• Gain insight into how Tabnine’s AI analyzes their code to deliver refined suggestions.

This instructor-led, live training in Germany (online or onsite) is designed for advanced developers and team leads seeking to master the advanced features of Tabnine.

Upon completing this training, participants will be able to:

• Deploy Tabnine within complex software projects.
• Tailor and train Tabnine's AI models for specific applications.
• Incorporate Tabnine into team workflows and development pipelines.
• Improve code quality and speed up development cycles leveraging Tabnine's insights.

Delivered live by an instructor, this training in Germany (available online or onsite) is tailored for developers from entry-level to expert who aim to utilize AI for code generation through Tabnine.

By the conclusion of this session, attendees will possess the ability to:

• Comprehend the core principles of AI-assisted code creation.
• Set up and adjust Tabnine within their specific development setup.
• Apply Tabnine for streamlined code completion and precise error fixing.
• Build and train bespoke AI models via Tabnine for niche coding challenges.

This instructor-led, live training (online or onsite) is designed for intermediate Python developers and data scientists aiming to elevate their productivity using Tabnine.

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

• Set up and configure Tabnine within their Python development workspace.
• Utilize Tabnine's autocomplete tools to code more efficiently.
• Adjust Tabnine's behavior to match their coding preferences and project goals.
• Comprehend the specific interaction between Tabnine's AI model and Python code.