Issue #164 - THE ML ENGINEER ??

This #164 edition of the ML Engineer newsletter contains curated articles, tutorials and blog posts from experienced Machine Learning and MLOps professionals. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions ??

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This week in Issue #164: ?

• DeepMind AI Developer AlphaCode
• Databases 2022 Year in Review
• Evolving Notebook Infra at Twitter
• Doordash Feature Eng System
• O'Reilly on Causal Inference
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more ??

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

DeepMind AI Dev AlphaCode

Absolutely fascinating project from DeepMind that shows promising results for an AI based system that is able to tackle programming/algorithmic challenges. The article provides fascinating insights together with even attention visualisation of the algorithm itself as it tackles a specific challenge description.

Databases 2022 Year in Review

A great resource that provides a comprehensive overview of the state of databses in 2021, showing key insights including the dominance of Postgres, benchmarking competition, increasing revenues, failings and more.

Evolving Notebook Infra at Twitter

A great article from Twitter Engineering providing an insight into the evolution of Jupyter Notebook infrastructure at scale, including some of their challenges, tooling, features, infrastructure, security, data sources, and what's next.

Doordash Feature Eng System

Feature engineering is a growingly critical requirements in the MLOps lifecycle - Doordash engineering presents their internal system "Fabricator". In this article they provide an architectural and conceptual overview of their system, together with their requirements, data & control planes, features, examples, limitations, and future developments.

O'Reilly on Causal Inference

The AI O'Reilly team has put together a great introduction into Causal Inference, where they provide a high level overview, together with useful resources, as well as insights across a broad range of sub-fields.

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:

• 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 !

About us: The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning. Check out our website