MLOps Practical in depth hands-on MLOPs utilising best available tools [known in 2022], practice and strategy. 01 Intro to MLOps, Initial Setup, and Docker 02 Hydra, Project Templates 03 DVC - Data Version Control and Experiment Tracking Reference implemetation in repo https://github.com/aiplaybookin/lightning-hydra-template 04 Deployemnt for Demos Gradio App (or Streamlit) APIs (testing using hoppscotch) Torch Script vrs Trace 05 AWS - Training & Deployments EC2 S3 ECS ECR Spot Instances, EKS, Lambda, Kinesis, Firehose, Sagemaker 06 Distributed Training and Case Study 07 Model Explainability 08 Model Serving with Torch Serve 09 Deployment on Accelerators (AWS Inf) and Serverless Inference 10 Deployment on Edge Devices (Jetson nano) 11 Model and Data Drift 12 AWS Serverless Best Practices 13 Kubeflow, Sagemaker Pipelines and Kafka 14 CI/CD Pipeline with AWS/Jenkins