AI for Software Development Training in Richmond

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 is an AI-powered coding assistant that helps automate development tasks, including DevOps operations such as writing YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to use GitHub Copilot to streamline DevOps tasks, improve automation, and boost productivity.

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

• Use GitHub Copilot to assist with shell scripting, configuration, and CI/CD pipelines.
• Leverage AI code completion in YAML files and GitHub Actions.
• Accelerate testing, deployment, and automation workflows.
• Apply Copilot responsibly with an understanding of AI limitations and best practices.

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.

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 Richmond (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 Richmond (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 Richmond (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.

This instructor-led, live training in Richmond (online or onsite) is aimed at intermediate-level front-end developers who wish to use GitHub Copilot to automate repetitive coding tasks, improve UI/UX design, and streamline front-end workflows.

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

• Set up GitHub Copilot for front-end development projects.
• Leverage Copilot to generate HTML, CSS, and JavaScript code efficiently.
• Improve UI/UX design processes using AI-generated code suggestions.
• Enhance front-end workflows with practical Copilot integration strategies.
• Troubleshoot and debug front-end code using Copilot assistance.

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.

This instructor-led, live training in Richmond (online or onsite) is aimed at beginner-level to intermediate-level Python developers who wish to leverage GitHub Copilot for Python-specific tasks, debugging, and implementing machine learning workflows.

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

• Set up and configure GitHub Copilot for Python development.
• Leverage Copilot to write efficient Python code.
• Debug Python applications using AI-generated suggestions.
• Automate repetitive coding tasks and improve workflow efficiency.
• Utilize Copilot for implementing machine learning projects in Python.

This instructor-led, live training in Richmond (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.

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 Richmond (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.

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.

This instructor-led, live training in Richmond (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 Richmond (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 Richmond (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 Richmond (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.

This instructor-led, live training in Richmond (online or onsite) is aimed at beginner-level developers who wish to understand GitHub Copilot's capabilities, set it up, and use it effectively to enhance their coding experience.

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

• Understand what GitHub Copilot is and how it works.
• Set up GitHub Copilot with a supported code editor.
• Use GitHub Copilot to write, refactor, and debug code faster.
• Leverage Copilot to explore coding techniques and solutions.
• Apply best practices for integrating GitHub Copilot into daily workflows.

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.

This instructor-led, live training in Richmond (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.

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

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

• Understand the capabilities and benefits of GitHub Copilot.
• Configure and integrate Copilot into a development workflow.
• Understand Copilot advanced features and learn best practices in using Copilot effectively.

This instructor-led, live training in Richmond (online or onsite) is aimed at beginner-level developers who wish to increase 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 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.