By David Santana | 128 Min Video

Watch this insightful webinar that explores how GitHub Copilot can supercharge the development of Generative AI model pipelines. We provide an in-depth look at the fine-tuning process, helping you understand its importance in optimizing model performance.

Learn when and why to fine-tune, along with various techniques that can improve the efficiency of your AI models. We also cover practical implementation strategies to effectively apply these techniques in your workflows.

Whether you’re an AI engineer, Deep Learning Engineer, Data Scientist, MLOps Engineer, or Developer looking to refine your Generative AI projects, this session will equip you with the knowledge and tools to take your model pipelines to the next level. Participants will leave with a deeper understanding of when and how to fine-tune models, guided strategies for automating pipeline tasks, and hands-on experience embedding Copilot into development workflows.

Don’t miss this opportunity to enhance your AI development skills with cutting-edge tools and techniques!

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Using GitHub Copilot for Pipeline Development

• Code generation: Demonstrates Copilot’s role in automating boilerplate code, helper functions, and data transformation tasks within ML pipelines.
• Prompting strategies: Covers best practices for writing prompts that guide Copilot to produce accurate and context-aware code snippets.

Fine-Tuning Techniques with Automation

• Importance of fine-tuning: Explains why fine-tuning is essential to customize pre-trained models for specific use cases, enhancing performance and relevance.
• Copilot-supported coding: Shows how Copilot can write reproducible and standardized fine-tuning scripts (e.g., data preprocessing, training loops, evaluation metrics), ensuring consistency and reducing manual errors.

Integrating Copilot into MLOps Pipelines

• Automation workflows: Demonstrates embedding Copilot-generated code into CI/CD flows using GitHub Actions, enabling automated testing, training, versioning, and deployment.
• Pipeline efficiency: Discusses how Copilot accelerates initial pipeline creation and streamlines enhancements during iteration.

Key Takeaways

• GitHub Copilot acts as a powerful pair programmer for MLOps tasks, speeding up coding by handling repetitive and boilerplate work.
• Fine-tuning is central to model optimization; Copilot can standardize and automate these scripts.
• Embedding Copilot within CI/CD enables reproducible, version-controlled, and automated ML pipelines.
• The session equips practitioners with both conceptual insights and actionable templates to enhance productivity and pipeline robustness.

Instructor Bio:

David is an AI & Data Multi-Cloud architect and executive engineer, certified trainer, course developer, and cloud evangelist. David’s vision is to drive digital innovation to modernize application frameworks and services. David’s passion for software engineering and data science has made him the lead developer, and lead trainer in various cross-industry projects