About Course

This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action

Course Syllabus

Module - 1 : Introduction

What is MLOps ,MLOps maturity model ,Running example: NY Taxi trips dataset , Why do we need MLOps , Course overview , Environment preparation

Module-2 : Experiment tracking and model management

Experiment tracking intro , Getting started with MLflow , Experiment tracking with MLflow , Saving and loading models with MLflow , Model registry , MLflow in practice

Module - 3 : Orchestration and ML Pipelines

Workflow orchestration , Prefect 2.0 , Turning a notebook into a pipeline , Deployment of Prefect flow

Module - 4 : Model Deployment

Three ways of model deployment: Online (web and streaming) and offline (batch) , Web service: model deployment with Flask , Streaming: consuming events with AWS Kinesis and Lambda , Batch: scoring data offline

Module - 5 : Model Monitoring

Monitoring ML-based services , Monitoring web services with Prometheus, Evidently, and Grafana , Monitoring batch jobs with Prefect, MongoDB, and Evidently

References

1.https://github.com/DataTalksClub/mlops-zoomcamp 2.projects: https://github.com/DataTalksClub/mlops-zoomcamp/tree/main/07-project