https://github.com/nathadriele/mlops-zoomcamp

The Zoomcamp MLOps Course covers tools like MLflow, Mage, Flask, Prometheus, Evidently, Grafana, Prefect, Terraform, and GitHub Actions. It emphasizes experiment tracking, model deployment, monitoring, CI/CD, and orchestration, culminating in an end-to-end project integrating best practices in MLOps.
https://github.com/nathadriele/mlops-zoomcamp

aws batch-processing flask grafana mage-ai mlflow mlops model mongodb monitoring orchestration prefect streaming

Last synced: 6 months ago
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The Zoomcamp MLOps Course covers tools like MLflow, Mage, Flask, Prometheus, Evidently, Grafana, Prefect, Terraform, and GitHub Actions. It emphasizes experiment tracking, model deployment, monitoring, CI/CD, and orchestration, culminating in an end-to-end project integrating best practices in MLOps.

• Host: GitHub
• URL: https://github.com/nathadriele/mlops-zoomcamp
• Owner: nathadriele
• Created: 2024-05-18T18:55:35.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-18T15:38:08.000Z (8 months ago)
• Last Synced: 2025-03-24T14:16:44.598Z (6 months ago)
• Topics: aws, batch-processing, flask, grafana, mage-ai, mlflow, mlops, model, mongodb, monitoring, orchestration, prefect, streaming
• Language: Jupyter Notebook
• Homepage:
• Size: 88.4 MB
• Stars: 10
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Zoomcamp MLOps Course

[More details](https://github.com/DataTalksClub/mlops-zoomcamp)

🟢 **Final project developed**

https://github.com/nathadriele/vercel-app-mlops-zoomcamp-project-paris-price-house

![image](https://github.com/user-attachments/assets/0509c5c9-e854-4adc-b33f-b0fce23892c6)

## 1. Introduction

- 1.1 What is MLOps

- 1.2 MLOps maturity model

- 1.3 Running example: NY Taxi trips dataset

- 1.4 Why do we need MLOps

- 1.5 Environment preparation

## 2. Experiment tracking and model management

- 2.1 Experiment tracking intro

- 2.2 Getting started with MLflow

- 2.3 Experiment tracking with MLflow

- 2.4 Saving and loading models with MLflow

- 2.5 Model registry

- 2.6 MLflow in practice

## 3. Orchestration and ML Pipelines

- 3.1 Workflow orchestration

- 3.2 Mage

## 4. Model Deployment

- 4.1 Three ways of model deployment: Online (web and streaming) and offline (batch)

- 4.2 Web service: model deployment with Flask

- 4.3 Streaming: consuming events with AWS Kinesis and Lambda

- 4.4 Batch: scoring data offline

## 5. Model Monitoring

- 5.1 Monitoring ML-based services

- 5.2 Monitoring web services with Prometheus, Evidently, and Grafana

- 5.3 Monitoring batch jobs with Prefect, MongoDB, and Evidently

## 6. Best Practices

- 6.1 Testing: unit, integration

- 6.2 Python: linting and formatting

- 6.3 Pre-commit hooks and makefiles

- 6.4 CI/CD (GitHub Actions)

- 6.5 Infrastructure as code (Terraform)

## 7. Project

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