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THE ML ENGINEER — WEEKLY NEWSLETTER

The MachineLearning EngineerIssue #43

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Issue #43 🤖 - IEML joins the Linux Foundation, Case studies with NumFocus, ML for business & ops intelligence, The open FairML Book, AI Ethics - whose ethics? + more 🚀

This week in Issue #43:

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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!

IEML joins the Linux Foundation

The Institute for Ethical AI & Machine Learning (the non-profit behind this newsletter) is thrilled to be joining the Linux Foundation’s LFAI as an organisational member! Some of our core work has already made its way to LF initiatives, including large sections of our Awesome Production ML List contributing to the fast-growing LF AI Landscape. We will be involved across various workstreams within the Linux Foundation, and will be contributing across the board to their ML initiatives. Exciting times ahead, and a lot of even more exciting news in future newsletter editions!

Case studies with NumFocus

We’ve heard incredible achievements in the research community, ranging from creating the world’s first picture of a black hole, to finding cure to diseases. NumFocus is the organisation behind the open source tools that have been enabling some of these great achievements, including NumPy, SKlearn, Jupyer, Pandas, and many more. Recently NumFocus launched an initiative where they created a set of case studies where they showcase achievements accomplished using NumFocus tools, including the black hole photograph, curing diseases and introducing transparency into AI algorithms.

ML for business & ops intelligence

O’Reilly Chief Scientist Ben Lorica comes back with yet another great podcast where he speaks with Peter Bailis, Co-founder of Stanford’s DAWN Lab and CEO of Sisu, a startup that is using machine learning to improve operational analytics. In this podcast they dive into the role of ML in operational analytics, ML Benchmark initiatives (such as MLPerf and DAWNBench), and trends in tools for the lifecycle of ML in the enterprise.

The open FairML Book

With the rise of AI, learning machine learning concepts has become critical. However the lack of resources around the social challenges which ML practitioners may face is not significant. Three researchers from Cornell, Berkeley and Princeton came together to write a non-exhaustive but comprehensible book that contains key insights to consider “Fairness” as core throughout the development of ML-related systems, as opposed to as an afterthought. The book is still work in progress, but there are a couple of key chapters available for free.

AI Ethics - whose ethics?

As AI becomes more prevalent in society, we face thougher challenges around privacy, security and trust of systems. These challenges often create scenarios that may raise ethical questions which practitioners and leaders will have to tackle. Because of this, learning and studying the underlying philosophical concepts that have been built throughout the millenia could provide incredibly positive results. We started a lunch group in London to dive into these topics once a month. Last session Dr. Ryan Dawson provided in introducion on Aristotle’s Nichomachean Ethics, which followed by a discussion around their relevance in today’s connected world. Next session’s topic is “Whose Ethics?” where we’ll be diving into the similarities and differences of Western and Eastern philosophy and its modern relevance into AI Ethics.

OSS: ML Deployment Libraries

The theme for this week’s featured ML libraries is Machine learning Deployment and Orchestration Libraries, and we’re happy to share brand new libraries into that section. The four featured libraries this week are:

  • Seldon - Open source platform for deploying and monitoring machine learning models in kubernetes
  • KFServing - Serverless framework to deploy and monitor machine learning models in Kubernetes
  • Redis-AI - A Redis module for serving tensors and executing deep learning models. Expect changes in the API and internals.
  • Model Server for Apache MXNet (MMS) - A model server for Apache MXNet from Amazon Web Services that is able to run MXNet models as well as Gluon models (Amazon’s SageMaker runs a custom version of MMS under the hood)

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!

MLConf = Conferences & Events

We feature conferences that have core ML tracks (primarily in Europe for now) to help our community stay up to date with great events coming up.

Technical & Scientific Conferences

Business Conferences

MLJobs = Jobs & Careers

We showcase Machine Learning Engineering jobs (primarily in London for now) to help our community stay up to date with great opportunities that come up.

Leadership Opportunities

Mid-level Opportunities

Junior Opportunities