Continuous Integration and Delivery for AI

Automate, Deploy, and Scale: CI/CD Pipelines for AI Workflows

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Enroll now for early access of e-LMS

MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
3 Weeks

About

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.

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.

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.

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

Participant’s Eligibility

AI engineers, machine learning specialists, DevOps professionals focusing on automating AI workflows.

Program Outcomes

Master the automation of machine learning workflows using CI/CD principles.
Proficiency in setting up scalable AI model pipelines for real-time operations.
Ability to monitor, retrain, and update models continuously in production environments.
Hands-on experience in building and deploying CI/CD pipelines for machine learning.

Fee Structure

Standard Fee: INR ~~4,998~~ USD 78

Discounted Fee: INR 2499 USD 39

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

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Key Takeaways

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.