Course Wiki

Docker

Container packaging for model services, pipeline components, and app deployments across four Zoomcamps.

Docker turns an application plus its dependencies into a portable image. Data Engineering Zoomcamp introduces it first: Postgres, pgAdmin, and later pipeline components all run as containers defined in docker-compose files, so a local environment mirrors production.

Machine Learning Zoomcamp uses the same skill to package a FastAPI model service into an image that runs identically on a laptop and in the cloud. MLOps Zoomcamp deploys Flask web services in containers and runs integration tests with docker-compose; AI Dev Tools Zoomcamp containerizes the full-stack app with Docker Compose and Postgres before deploying it to AWS. The throughline: if it runs in the container, it runs anywhere.

Taught in

• Machine Learning Zoomcamp

  • Module 1: Introduction to Machine Learning
    • Setting up the Environment
  • Module 5: Deploying Machine Learning Models
    • Intro / Session overview
    • Python virtual environment: Pipenv
    • Environment management: Docker
    • Deployment to the cloud: AWS Elastic Beanstalk (optional)
  • Module 9: Serverless Deep Learning
    • TensorFlow Lite
    • Preparing a Docker image
    • Python 3.12 vs TF Lite 2.17
  • Module 10: Kubernetes and TensorFlow Serving
    • Overview
    • Creating a pre-processing service
    • Running everything locally with Docker-compose
    • Introduction to Kubernetes

• LLM Zoomcamp

  • Module 1: Agentic RAG
    • Environment
    • Search
  • Module 2: Vector Search
    • Vector Search
    • Embeddings
    • Vector Search with PGVector
    • Using ONNX Runtime instead of PyTorch
  • Module 3: Orchestration
    • Setting up Kestra
    • Retrieval Augmented Generation
    • AI Agents
  • Module 5: Monitoring
    • Storing Data in PostgreSQL
    • Streamlit Dashboard
    • Synthetic Data Generation
    • Grafana Dashboards
    • Docker Compose
    • Next Steps
  • Module 7: End-to-End Project
    • End-to-End Project Example
    • Monitoring and Containerization
    • Summary and Closing Remarks

• AI Dev Tools Zoomcamp

  • Module 3: Test, Containerize, and Deploy an AI-Assisted App
    • Test, Containerize, and Deploy an AI-Assisted App

• MLOps Zoomcamp

  • Module 1: Introduction
    • 2 VM in AWS
    • Homework
  • Module 2: Experiment Tracking & Model Management
    • Homework
  • Module 3: Orchestration & ML Pipelines
    • Homework
  • Module 4: Model Deployment
    • Web-services: Deploying models with Flask and Docker
  • Module 5: Model Monitoring
    • Debugging with test suites and reports
  • Module 6: Best Practices
    • Integration tests with docker-compose
    • Homework

• Data Engineering Zoomcamp

  • Module 3: Data Warehousing
    • Deploying Machine Learning model from BigQuery
  • Module 4: Analytics Engineering
    • Analytics Engineering ## Related concepts

• FastAPI

• CI/CD

• Model Deployment

• Terraform

• Serverless Deployment