Aim

This program is designed to equip AI professionals and DevOps engineers with the skills needed to integrate continuous integration (CI) and continuous delivery (CD) into AI and machine learning workflows. The course focuses on automating model building, testing, and deployment to ensure smooth, scalable AI operations.

Program Objectives

CI/CD for AI: Understand the fundamentals of CI/CD and their application to AI/ML workflows.
Automation in AI Workflows: Learn to automate data preprocessing, model training, and deployment.
Implement CI/CD Pipelines: Build robust pipelines for automating AI workflows.
Continuous Monitoring: Establish best practices for monitoring AI models and automating retraining.
Hands-On Experience: Gain practical knowledge by setting up scalable CI/CD pipelines for real-time AI deployment.

Program Structure

Module 1: Introduction to CI/CD in AI

Principles of CI/CD in AI/ML
The importance of automation in machine learning workflows
Benefits of continuous integration and delivery in AI

Module 2: Building Automated Pipelines

Automating data preprocessing and feature engineering
Managing and versioning datasets
Setting up continuous pipelines for model training

Module 3: Continuous Integration for Machine Learning

Implementing automated testing for AI models
Integrating version control (Git) in AI workflows
Tools for automated builds (Jenkins, GitLab CI)

Module 4: Continuous Delivery in AI

Automating model deployment to production environments
CI/CD pipelines for cloud platforms (AWS, GCP, Azure)
Model deployment using Kubernetes, Docker, and Terraform

Module 5: Model Monitoring and Feedback Loops

Monitoring model performance post-deployment
Setting up automated alerts and retraining workflows
Managing feedback loops for AI models

Module 6: Best Practices for Scalable AI CI/CD Pipelines

Handling model drift and updating models in production
Optimizing scalability in AI CI/CD pipelines
Case Studies: CI/CD in large-scale AI deployments

Module 7: Hands-On Project: Implementing an AI CI/CD Pipeline

End-to-end implementation of an AI CI/CD pipeline
Automating model testing, deployment, and monitoring
Documenting and presenting the CI/CD workflow

Participant’s Eligibility

AI Engineers looking to streamline AI model deployment.
Machine Learning Specialists focused on workflow automation.
DevOps Professionals aiming to automate AI workflows.

Program Outcomes

CI/CD Mastery: Automation of machine learning workflows using CI/CD principles.
Scalable Pipelines: Proficiency in building AI pipelines for real-time operations.
Model Management: Ability to monitor, retrain, and update AI models continuously in production.
Hands-On Experience: Practical knowledge in building CI/CD pipelines for machine learning.

Program Deliverables

e-LMS Access: All course materials and resources.
Real-Time Project: Practical experience setting up a full CI/CD pipeline for an AI model.
Guidance: Professional mentorship for project development.
Certification: Upon successful completion of all assessments.

Future Career Prospects

MLOps Engineer
DevOps Specialist for AI Workflows
Continuous Delivery Engineer
Cloud AI Architect
AI Infrastructure Engineer

Job Opportunities

AI companies needing scalable CI/CD solutions for model deployment.
Enterprises automating AI workflows through CI/CD pipelines.
Cloud computing providers offering CI/CD services for AI-driven platforms.
AI infrastructure companies focused on automation tools and pipelines.

MODE	Online/ e-LMS
TYPE	Self Paced
LEVEL	Moderate
DURATION	3 Weeks

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Continuous Integration and Delivery for AI Course – 3 Weeks

Aim

Program Objectives