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
AI Model Deployment and Serving closes the gap between a trained model and a running service. You learn to package a model, wrap it in an API, and containerise it with Docker for reproducible deployment. The course covers real-time and batch serving, scaling under load, and the operational concerns that decide whether a model survives production: versioning, monitoring, latency, and detecting data and concept drift. Framed by MLOps practice, it also covers CI/CD for models and safe rollout strategies. You leave able to deploy, serve and maintain a model as a dependable service. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers deploying and serving machine-learning models in production — packaging, APIs, containers, scaling, monitoring and the MLOps lifecycle.
📋 Course Objectives
1. Package and containerise a model for deployment.
2. Serve models via real-time APIs and batch jobs.
3. Scale serving and manage latency under load.
4. Monitor models and detect data and concept drift.
5. Apply MLOps practices: versioning, CI/CD and safe rollout.
👥 Who Should Enroll?
• ML engineers moving models to production
• Data scientists learning to ship their work
• DevOps engineers supporting ML systems
• Students specialising in MLOps
🚀 Key Learning Outcomes
• The ability to deploy a model as a production service.
• A containerised, monitored serving project.
• MLOps skills that make models durable.
• 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 Fundamentals, Mathematics, and AI Model Deployment and Serving Foundations
Develop a comprehensive understanding of AI fundamentals, including machine learning, deep learning, and neural networks • Analyze mathematical concepts, such as linear algebra, calculus, and probability, and their applications in AI model deployment • Design a basic AI model using popular frameworks, such as TensorFlow or PyTorch, and deploy it on a cloud platform
Data Engineering, Preprocessing, and Feature Pipelines
Configure data pipelines using tools, such as Apache Beam or AWS Glue, to preprocess and transform raw data into usable formats • Implement data quality checks and data validation techniques to ensure data integrity and accuracy • Develop a feature engineering pipeline using techniques, such as feature scaling, encoding, and selection, to improve model performance
Model Architecture, Algorithm Design, and AI Model Deployment and Serving Methods
Evaluate different model architectures, such as convolutional neural networks or recurrent neural networks, for various AI tasks • Design and implement custom algorithmic solutions using popular libraries, such as scikit-learn or Keras • Optimize model performance using techniques, such as hyperparameter tuning, regularization, and early stopping
Training, Hyperparameter Optimization, and Evaluation
Train AI models using various optimization algorithms, such as stochastic gradient descent or Adam • Implement hyperparameter optimization techniques, such as grid search or Bayesian optimization, to improve model performance • Develop a model evaluation framework using metrics, such as accuracy, precision, or F1-score, to assess model quality
Deployment, MLOps, and Production Workflows
Deploy AI models on cloud platforms, such as AWS SageMaker or Google Cloud AI Platform, using containerization tools, such as Docker • Implement MLOps practices, such as continuous integration and continuous deployment, to streamline model deployment and monitoring • Develop a production-ready workflow using tools, such as Apache Airflow or Kubernetes, to automate model deployment and serving
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze AI systems for bias and fairness using techniques, such as data auditing or model interpretability • Develop strategies to mitigate bias and ensure fairness in AI decision-making using techniques, such as data preprocessing or model regularization • Implement responsible AI practices, such as transparency, explainability, and accountability, to ensure trustworthy AI systems
Industry Integration, Business Applications, and Case Studies
Evaluate AI applications in various industries, such as healthcare, finance, or retail, and identify opportunities for AI adoption • Develop a business case for AI adoption using cost-benefit analysis and return on investment calculations • Analyze real-world case studies of AI implementation and identify best practices for successful AI deployment
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | Docker |
| Covered Tool / Platform | Kubernetes |
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