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Course Project

The goal of this project is to apply everything we learned in this course and build an end-to-end machine learning project.

Remember that you must evaluate three peers. If you don't do that, your project can't be considered complete.

Problem statement

For the project, we will ask you to build an end-to-end ML project.

For that, you will need:

  • Select a dataset that you're interested in (see datasets.md)
  • Train a model on that dataset tracking your experiments
  • Create a model training pipeline
  • Deploy the model in batch, web service or streaming
  • Monitor the performance of your model
  • Follow the best practices

Technologies

You don't have to limit yourself to technologies covered in the course. You can use alternatives as well:

  • Cloud: AWS, GCP, Azure or others
  • Experiment tracking tools: MLFlow, Weights & Biases, ...
  • Workflow orchestration: Prefect, Airflow, Flyte, Kubeflow, Argo, ...
  • Monitoring: Evidently, WhyLabs/whylogs, ...
  • CI/CD: Github actions, Gitlab CI/CD, ...
  • Infrastructure as code (IaC): Terraform, Pulumi, Cloud Formation, ...

If you use something that wasn't covered in the course, be sure to explain what the tool does.

If you're not certain about some tools, ask in Slack.

Peer review criteria

  • Problem description
    • 0 points: The problem is not described
    • 1 point: The problem is described but shortly or not clearly
    • 2 points: The problem is well described and it's clear what the problem the project solves
  • Cloud
    • 0 points: Cloud is not used, things run only locally
    • 2 points: The project is developed on the cloud OR uses localstack (or similar tool) OR the project is deployed to Kubernetes or similar container management platforms
    • 4 points: The project is developed on the cloud and IaC tools are used for provisioning the infrastructure
  • Experiment tracking and model registry
    • 0 points: No experiment tracking or model registry
    • 2 points: Experiments are tracked or models are registered in the registry
    • 4 points: Both experiment tracking and model registry are used
  • Workflow orchestration
    • 0 points: No workflow orchestration
    • 2 points: Basic workflow orchestration
    • 4 points: Fully deployed workflow
  • Model deployment
    • 0 points: Model is not deployed
    • 2 points: Model is deployed but only locally
    • 4 points: The model deployment code is containerized and could be deployed to cloud or special tools for model deployment are used
  • Model monitoring
    • 0 points: No model monitoring
    • 2 points: Basic model monitoring that calculates and reports metrics
    • 4 points: Comprehensive model monitoring that sends alerts or runs a conditional workflow (e.g. retraining, generating debugging dashboard, switching to a different model) if the defined metrics threshold is violated
  • Reproducibility
    • 0 points: No instructions on how to run code at all, the data is missing
    • 2 points: Some instructions are there, but they are not complete OR instructions are clear and complete, the code works, but the data is missing
    • 4 points: Instructions are clear, it's easy to run the code, and it works. The versions for all the dependencies are specified.
  • Best practices
    • There are unit tests (1 point)
    • There is an integration test (1 point)
    • Linter and/or code formatter are used (1 point)
    • There's a Makefile (1 point)
    • There are pre-commit hooks (1 point)
    • There's a CI/CD pipeline (2 points)