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.
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