Issue #178 - THE ML ENGINEER ??

This #178 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 ??

If you like the content please support the newsletter by sharing on ?? Twitter,??? Linkedin and??? Facebook!

This week in Issue #178:?

• Enterprise Declarative ML System
• DeepMind's Generalist Agent
• Alibaba Scale Recommenders
• Product Success DS Guide
• Transformers from Scratch
• 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!

Enterprise Declarative ML System

The team behind the OSS declarative ML framework Ludwig has launched an enterprise platform with an insightful article that covers some of the motivations, features and plans to grow their success in the open core space.

Alibaba Scale Recommenders

An interesting overview of Alibaba's production machine learning system architecture and approach to recommendations at massive-scale, including some of the underlying techniques as well as systems design for powering recommendations at scale.

DeepMind's Generalist Agent

The DeepMind team has released an interesting new large model under the name "Gato" which is claimed to provide multi-modal, multi-task, multi-embodiment capabilities. In this article they provide some of the context, concepts and resources for this new large model.

Product Success DS Guide

A practical guide from Shopify on data science techniques for measuring product success metrics, covering best practices, practical approaches and key takeaways.

Transformers from Scratch

A comprehensive end to end guide that covers the concept of transformers from scratch, building on the foundational concepts towards transformers in machine learning.

Open Source MLOps Tools

Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ? github stars. 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. Four featured libraries in the GPU acceleration space 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