Course Contents

• Model Deployment and Scaling: Deploying models on cloud platforms (e.g., AWS, Azure), Model versioning and serving with Docker and Kubernetes
• Transfer Learning and Fine-tuning: Leveraging pre-trained models, Fine-tuning models for custom applications
• Ensemble Techniques: Bagging and boosting algorithms, Building ensemble models for improved performance
• Case studies from specific industries (healthcare, finance, e-commerce and other domains)
• A term-long project where learners will grapple with an open-ended problem, and present their solutions.