Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Continuous Integration and Delivery for AI focuses on one crucial slice of the ML lifecycle: automating the path from code and data to a tested, shipped model. You learn how CI/CD adapts to machine learning — automated testing of data and models, reproducible build pipelines, versioning, and safe automated deployment with rollback. The course centres on the pipeline discipline that makes ML delivery fast and reliable rather than manual and fragile. You finish able to design a CI/CD pipeline for machine learning. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers continuous integration and delivery for AI — applying CI/CD practices to machine learning so models are tested, built and shipped reliably and automatically.
📋 Course Objectives
1. Adapt CI/CD principles to machine learning.
2. Automate testing of data and models.
3. Build reproducible ML pipelines.
4. Version code, data and models.
5. Deploy safely with automated rollback.
👥 Who Should Enroll?
• ML and platform engineers
• DevOps moving into ML
• Data scientists shipping models
• Students of MLOps
🚀 Key Learning Outcomes
• The ability to build ML CI/CD pipelines.
• A reliable ML-delivery perspective.
• An automation-focused workflow.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Foundations
Design scalable AI systems using containerization and orchestration tools like Docker and Kubernetes • Implement continuous integration pipelines using Jenkins and GitLab CI/CD for automated testing and deployment • Analyze AI project requirements and develop a comprehensive CI/CD strategy for improved collaboration and efficiency
Data Engineering and Preprocessing
Develop data preprocessing pipelines using Apache Beam and Apache Spark for efficient data processing and transformation • Configure data storage solutions like Amazon S3 and Google Cloud Storage for scalable data management • Evaluate data quality and implement data validation techniques using Great Expectations and Deequ
Model Architecture and Algorithm Design
Design and implement deep learning models using TensorFlow and PyTorch for computer vision and natural language processing tasks • Develop and evaluate machine learning algorithms using scikit-learn and XGBoost for regression, classification, and clustering tasks • Optimize model performance using hyperparameter tuning techniques like Grid Search and Random Search
Training, Hyperparameter Optimization, and Evaluation
Train and deploy machine learning models using Amazon SageMaker and Google Cloud AI Platform for scalable model deployment • Implement hyperparameter optimization techniques like Bayesian Optimization and Gradient-Based Optimization for improved model performance • Evaluate model performance using metrics like accuracy, precision, and recall, and develop strategies for model improvement
Deployment, MLOps, and Production Workflows
Deploy machine learning models using TensorFlow Serving and AWS SageMaker for scalable model deployment • Develop and implement MLOps workflows using Apache Airflow and Zapier for automated model deployment and monitoring • Configure model monitoring and logging solutions like Prometheus and Grafana for real-time model performance tracking
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and mitigate bias in machine learning models using techniques like data preprocessing and feature engineering • Develop and implement fairness metrics like disparity impact and equal opportunity difference for fair model evaluation • Evaluate and implement explainability techniques like SHAP and LIME for transparent model interpretation
Industry Integration, Business Applications, and Case Studies
Develop and implement AI solutions for business applications like customer segmentation and predictive maintenance • Evaluate and implement AI-powered chatbots using Dialogflow and Microsoft Bot Framework for improved customer service • Analyze and develop strategies for AI adoption in various industries like healthcare and finance
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Docker |
| Covered Tool / Platform | Kubernetes |
| Covered Tool / Platform | Jenkins |
| Covered Tool / Platform | GitLab CI/CD |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
