The MachineLearning EngineerIssue #117

This week in Issue #117:

• Starcraft AI Programming Tutorial
• The Netflix Cosmos Platform
• Scaling ETL Workers with K8s
• Production ML at Booking.com
• Kaggle Tricks & Best Practices
• Featured OSS Production ML Libraries
• Awesome AI Guidelines to check out this week
• + more 🚀

Forward email, or share the online version on 🐦 Twitter, 💼 Linkedin and 📕 Facebook!

If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone’s support!

Starcraft AI Programming Tutorial

An interesting excerpt of a lecture series on applied AI in games, which delves into a practical tutorial that teaches how to program your own Starcraft AI client. This resource provides a very comprehensive overview and intuition on how AI systems can be built against games, and although a full reinforcement-AI-based system is not developed within this lesson specifically, it will provide with the tools and directions to develop towards that milestone for anyone interested.

The Netflix Cosmos Platform

The Netflix infra engineering team shares learnings and best practices from their Cosmos platform, a system that leverages serverless “lambdas” to enable for the processing of resource-intensive algorithms coordinated via complex hierarchical workflows that can last from minutes to “years”.

Scaling ETL Workers with K8s

An excellent tutorial that covers a popular trend in the Kubernetes space, applied towards the intuition behind scaling ETL pipelines. It shows how to use the KEDA (Kubernetes Event-Driven Autoscaler) to scale a celery/rabbit-mq based ETL system.

Production ML at Booking.com

A broad overview of concepts, techniques and results from Booking.com’s production machine learning infrastructure and data science capabilities. It dives into the main “pillars” of mechanisms used to productionise models, as well as tradeoffs, architectures, and lessons learned.

Kaggle Tricks & Best Practices

A brief and consise overview of a set of tips, tricks and best practices that can be followed to tackle Kaggle (and general ML) challenges, including correlation matrices, missing values, dataframe styler, and more.

OSS: GPU Accel. Frameworks

The topic for this week’s featured production machine learning libraries is GPU Acceleration Frameworks. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are:

• Vulkan Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
• CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
• Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
• CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.

If you know of any libraries that are not in the “Awesome MLOps” list, please do give us a heads up or feel free to add a pull request!

OSS: Awesome AI Guidelines

As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:

• AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems
• An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles
• ACM’s Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018
• From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices

If you know of any guidelines that are not in the “Awesome AI Guidelines” list, please do give us a heads up or feel free to add a pull request!