/mlops-zoomcamp

mlops-zoomcamp

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mlops-zoomcamp by DataTalkClub

My Journery following the MLOPS ZoomCamp

MLOPS

Module 1: Introduction

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

Module 2: Experiment-Tracking

  • Experiment Tracking into
  • Getting started with MLflow
  • Experiment tracking with MLflow
  • Model management
  • Model registry
  • Mlflow in practice
  • Homework

Module 3: Orchestration

  • Workflow orchestration
  • Prefect 2.0
  • Turning a notebook into a pipeline
  • Deployment of Prefect Flow
  • Homework

Module 4: Deployment

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

Module 5: Model Monitoring

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

Module 6: Best Practices

  • Testing: unit, integration
  • Python: linting and formatting
  • Pre-commit hooks and makefiles
  • CI/CD (Github Actions)
  • Infrastructure as code (Terraform)
  • Homework

Project

  • End-to-end project with all the things above

Module 7: Processes

  • CRISP-DM, CRISP-ML
  • ML Canvas
  • Data Landscape canvas
  • MLOps Stack Canvas
  • Documentation practices in ML projects (Model Cards Toolkit)

(In October)

Instructors

  • Larysa Visengeriyeva
  • Cristian Martinez
  • Kevin Kho
  • Theofilos Papapanagiotou
  • Alexey Grigorev
  • Emeli Dral
  • Sejal Vaidya

Other courses from DataTalks.Club:

Made with 💟 by Dinesh Chopra