The MachineLearning EngineerIssue #146

This week in Issue #146:

• GPT2 with ONNX, Triton & Tempo
• 2021 Data/AI Salary Survey
• An Intuitive Deep Dive on Drift
• A Gentle Introduction to MLOps
• Julia Multi-Dispatch Effectiveness
• Open Source ML Frameworks
• 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!

GPT2 with ONNX, Triton & Tempo

Join us this week at both PyCon HK and/or NLP Summit 2021 where we’ll be giving keynote talks where we’ll cover how to accelerate NLP at scale with a GPT2 model using ONNX, Triton, Seldon Core & Tempo.

2021 Data/AI Salary Survey

AI O’Reilly’s Mike Loukides shares the results of a survey on salaries in the Data & AI space, providing some perspective on the job market for this growing field, and although it is currently geographically limited provides some insights on the sub-trends of challenges, gaps, opportunities and trends.

An Intuitive Deep Dive on Drift

The OSS Alibi Detect project has published a comprehensive introduction and overview of the drift detection which covers the high level concepts required to understand some of the key components, as well as relevant references to dive into more detail for any practitioners interested.

A Gentle Introduction to MLOps

A high level article that provides an introduction to the fast-growing and broad field of MLOps, including a lot of images and diagrams to introduce new practitioners to the field and common concepts.

Julia Multi-Dispatch Effectiveness

Julia Computing Co-Founder and CEO Viral Shah dives into conversation on the data exchange podcast where he covers interesting insights on the Julia language and community, as well as some updates on the goals and objectives with their recent Series A investment.

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!