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A curated awesome list of Machine Learning Engineering resources. Feel free to contribute! πŸš€

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Awesome Machine Learning Engineer

Awesome

For more awesomeness, check out Awesome.

What is this and how do I use it?

  • This is a curated list of delightful resources for everything you need to develop Machine Learning solutions.
  • All resources are structured as follows: [Content level] [Page title] - [Description] ([Reading time]).
    • There are three content levels:
      1. πŸ₯ Essential reading for all ML engineers
      2. 🐍 Advanced reading for professional ML engineers
      3. πŸ¦„ Expert material for expert ML engineers
    • Descriptions are written so that they complete the sentence "After reading this article you will have learned [to] ...".

Contents

Communication

Software Engineering

API design

Version control

Code review

Python patterns

Typing

Curated Python packages

Machine Learning

Practical theory

While, in theory, you can just download Tensorflow and start making deep neural networks, it doesn’t hurt to know some of the theory and philosophy that lies behind the algorithms that so many of us know and love today.

Pandas

Sklearn

DevOps

CI/CD

  • 🐍 invoke - Implement common tasks you run on your projects as a CLI. (30 min)

Package management

Containerization

Shell

Terraform

Infrastructure

Related awesome lists

To add

  • TODO: Mypy strict mode.
  • TODO: Raymond Hettinger
  • TODO: Gridsearch vs random search vs Bayesian hyperparam optimization (gaussian processes)
  • TODO: Comparison of bayesian hyperparam optimizers (PyGPGO)
  • TODO: conda vs virtualenv, pyenv, pipenv.
  • TODO: explain how conda-forge works.
  • TODO: explain registries (Docker Hub, ECR, GitLab)
  • TODO: explain environment.yml + interactions with Docker.
  • TODO: S3, DynamoDB, MongoDB
  • TODO: CVE scans (frontend and backend)
  • TODO: OSS license scan
  • TODO: mutual TLS, IP whitelisting, (VPN)
  • TODO: Kinesis streams
  • TODO: Linting built-in to Terraform with -check.
  • TODO: Tech in our pure cookiecutter scaffolding.
  • TODO: Cherry picking?
  • TODO: MLOps
  • TODO: KISS, DRY
  • TODO: mamba
  • TODO: pre-commit
  • TODO: selected Flake8 extensions
  • TODO: selected Pytest extensions
  • TODO: cookiecutter & cruft (as a standalone repo?)
  • TODO: https://pypi.org/project/snoop/
  • TODO: Slack etiquette

Curated by Radix

Radix is a Belgium-based Machine Learning company.

We invent, design and develop AI-powered software. Together with our clients, we identify which problems within organizations can be solved with AI, demonstrating the value of Artificial Intelligence for each problem.

Our team is constantly looking for novel and better-performing solutions and we challenge each other to come up with the best ideas for our clients and our company.

Here are some examples of what we do with Machine Learning, the technology behind AI:

  • Help job seekers find great jobs that match their expectations. On the Belgian Public Employment Service website, you can find our job recommendations based on your CV alone.
  • Help hospitals save time. We extract diagnosis from patient discharge letters.
  • Help publishers estimate their impact by detecting copycat articles.

We work hard and we have fun together. We foster a culture of collaboration, where each team member feels supported when taking on a challenge, and trusted when taking on responsibility.

radix

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A curated awesome list of Machine Learning Engineering resources. Feel free to contribute! πŸš€

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