Continuous Integration and Delivery for AI

Aim

Program Objectives

Program Structure

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

Who Should Enrol?

Program Outcomes

Fee Structure

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