Continuous Integration and Delivery for AI

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Mentor Based

Program Syllabus

Module 1

About This Course

This program covers the essential practices of CI/CD tailored for AI, ensuring that machine learning models can be continuously developed, tested, and deployed without disruptions. Participants will learn how to automate the entire ML pipeline, from data preprocessing and model training to deployment and monitoring.

Module 2

Aim

To provide AI professionals and DevOps engineers with advanced skills to integrate continuous integration (CI) and continuous delivery (CD) pipelines into AI and machine learning workflows. The program focuses on automating model building, testing, and deployment to ensure streamlined, scalable AI operations.

Module 3

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.

Module 4

Program Structure

Introduction to CI/CD in AI
- Key principles of CI/CD in machine learning
- Benefits of automation in AI workflows
Building Automated Pipelines
- Automating data preprocessing and feature engineering
- Versioning and managing datasets
- Setting up pipelines for continuous model training
Continuous Integration for Machine Learning
- Implementing automated testing for AI models
- Integration with Git for version control
- Tools for automated builds (Jenkins, GitLab CI)
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
Model Monitoring and Feedback Loops
- Monitoring model performance post-deployment
- Setting up alerts and retraining triggers
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
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

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.