ML Ops | Deployment Specialization - Learning Path

Learn to harness Docker for creating isolated, reproducible environments tailored to data science. Master building custom images, managing multi-container workflows, and deploying scalable applications.

Learn how to convert your ML models to a productionizable app. Host it in AWS EC2 instance performed live and learn all the intermediate steps, purpose behind it and best practices..

Learn how to serve machine learning from AWS Lambda and AWS Sagemaker. Undestand their services, set them up and learn how to deploy ML in AWS Lambda and AWS Sagemaker.

Learn to leverage distributed computing power for large-scale data processing, manipulate datasets efficiently, and harness advanced analytics techniques to derive meaningful insights.

Learn to streamline your machine learning workflows, track models effectively, and optimize deployment strategies for seamless integration into production environments.