Program Objectives:

• Understand CI/CD concepts and how they apply to AI/ML models.
• Automate data preprocessing, model training, and deployment workflows.
• Implement robust CI/CD pipelines for machine learning.
• Learn best practices for continuous monitoring and retraining of AI models.
• Gain hands-on experience in setting up scalable pipelines for real-time AI deployment.

What you will learn?

1. Introduction to CI/CD in AI
   • Key principles of CI/CD in machine learning
   • Benefits of automation in AI workflows
2. Building Automated Pipelines
   • Automating data preprocessing and feature engineering
   • Versioning and managing datasets
   • Setting up pipelines for continuous model training
3. Continuous Integration for Machine Learning
   • Implementing automated testing for AI models
   • Integration with Git for version control
   • Tools for automated builds (Jenkins, GitLab CI)
4. Continuous Delivery in AI
   • Automating model deployment to production environments
   • CI/CD pipelines for cloud platforms (AWS, GCP, Azure)
   • Deploying models with Kubernetes, Docker, and Terraform
5. Model Monitoring and Feedback Loops
   • Monitoring model performance post-deployment
   • Setting up alerts and retraining triggers
6. Best Practices for Scalable AI CI/CD Pipelines
   • Handling model drift, updating models in production
   • Case studies of CI/CD pipelines in large-scale AI deployments
7. Hands-on Project: Implementing an AI CI/CD Pipeline
   • Building a CI/CD pipeline for a machine learning model
   • Automating model testing, deployment, and monitoring