AI-Driven Software Development Lifecycle (SDLC) Training Course

This advanced training in New Zealand focuses on GitHub Copilot and AI applications for corporate infrastructure. It empowers managers to optimise project management, streamline workflows, and enhance decision-making using AI tools, with a particular emphasis on large-scale project requirements.

This instructor-led training in New Zealand enables advanced developers to excel in prompt engineering, fine-tuning, and custom tooling within Cursor. Participants will craft precise AI behaviours, integrate internal knowledge bases, and develop secure, domain-specific extensions to enhance enterprise workflows.

This instructor-led, live training in New Zealand (online or onsite) is aimed at advanced-level participants who wish to customise GitHub Copilot for team projects, utilise 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:

• Customise 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.
• Optimise team collaboration using AI-powered tools.
• Manage and troubleshoot Copilot settings and permissions effectively.

This instructor-led, live training in New Zealand (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 live training, offered online or onsite, targets intermediate software developers seeking to improve productivity and code quality by using AI-assisted coding with Cursor.

By the end of the session, participants will be able to:

• Install and configure Cursor for AI-assisted development.
• Integrate Cursor with Git repositories and existing workflows.
• Generate, debug, and optimise code via natural language.
• Leverage AI for refactoring, documentation, and testing tasks.

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

This instructor-led course in New Zealand assists developers in adopting Cursor, an AI-enhanced editor that increases productivity through smart completions and context-specific edits. Participants develop practical skills in refactoring, the secure review of AI suggestions, and team collaboration to optimise workflows.

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

This instructor-led, live training in New Zealand (online or on-site) is aimed at beginner 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 New Zealand (online or onsite) is aimed at intermediate-level to advanced-level participants who wish to optimise 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.
• Utilise Copilot to enhance collaborative coding practices.
• Optimise 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 New Zealand (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.
• Utilise Tabnine's autocomplete features to speed up coding.
• Customise 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 New Zealand (delivered online or on-site) is designed for advanced-level developers and team leads who aim to master Tabnine's advanced features.

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

• Deploy Tabnine within complex software projects.
• Customise 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 New Zealand (online or on-site) is tailored for developers at all skill levels—from beginners to experts—who want to harness AI for code generation using Tabnine.

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

• Grasp the fundamentals of AI-powered code generation.
• Install and configure Tabnine within their development environment.
• Leverage Tabnine for efficient code completion and error correction.
• Build and train custom AI models with Tabnine for specialised tasks.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level Python developers and data scientists looking to enhance their productivity with the help of Tabnine.

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

• Install and configure Tabnine within their Python development environment.
• Use Tabnine's autocomplete capabilities to write Python code more efficiently.
• Customise Tabnine's behaviour to align with their coding style and project requirements.
• Understand how Tabnine's AI model operates specifically with Python code.