Continuous Integration and Delivery for AI
Automate, Deploy, and Scale: CI/CD Pipelines for AI Workflows
Early access to e-LMS included
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.
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
Who Should Enrol?
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
Discounted: ₹8,499 | $112
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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