Online/ e-LMS
Mentor Based
Moderate
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
Fee: INR 8,499 USD 112
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 Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- MLOps Engineer
- DevOps Specialist for AI Workflows
- Continuous Delivery Engineer
- Cloud AI Architect
- AI Infrastructure Engineer
- Automation Engineer for AI Operations
Job Opportunities
- AI-driven organizations seeking scalable solutions for deploying models.
- Companies using CI/CD pipelines for automating machine learning workflows.
- Cloud computing providers offering AI-driven CI/CD services.
- AI infrastructure and tooling companies.
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