Subscribe to the Machine Learning Engineer Newsletter

Receive curated articles, tutorials and blog posts from experienced Machine Learning professionals.

Issue #23
The ML Engineer 🤖 has reached 1100+ subscribers 🚀 and the open source ML Engineering list has reached almost 600 stars 🔥 a massive thank you to all our subscribers and community members for all your support ✨👏🎉😃
 This week in Issue #23:
Google's best practices for ML Engineering, the journal of open source, visualising attention in deep NLP, tensorflow pruning API, ML explanations with VIBI, missing links in Serverless, data visualisation libraries, AI conferences, ML jobs and more 🚀
Support the ML Engineer!
Forward the 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! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
Google has put together a great Machine Learning Crash Course (with Tensorflow APIs) which comes together with 43 rules of Machine Learning. Great resource to get started quickly with practical real-life resources, as well as best practices to take into consideration when applying these learnings in industry.
Awesome on-line journal that has aggregated over 500 papers with open source code and made available online with stats such as downloads, repositories, data preview, citations and more. Projects like this (such as Papers with Code) are bringing huge value by introducing better reproducibility of experiments.
Attention is one of the breakthroughs that have enabled deep learning to continue revolutionising multiple areas. This very comprehensible article covers this topic extensible through very intuitive visualisations that show how attention is introduced in machine translation (deep NLP) machine learning models.
Weight pruning is a very promising technique in deep learning that basically allows to reduce the number of parameters and operations involved in a neural network by removing connections between neurons. This approach has massive potential as it makes the networks less complex, and hence more efficient (and theoretically easier to interpret). This post covers the tensorflow pruning API, so you can get started applying this technique.
AI explainability is one of the hottest topics of 2019, which have brought incredible insightful approaches. Researchers from CMU bring this week an interesting approach towards AI explainability through a concept based on the information bottleneck principle which defines what we mean by "good" representation (i.e. maximally informative about the output while compressive about a given input). In this paper they propose VIBI, or variational information bottleneck for interpretation, a system agnostic information bottleneck that provides a brief but comprehensive explanation for every single decision made by a black box.
Serverless has promised (and delivered) quite a lot of great value for cloud computing, and is currently entering the world of ML serving incredibly fast. This is a great article that highlights two very important features that serverless needs to conquer before it can be used in many more domains: stateful computation and communication-aware funcion placement.
MLOps = Featured OS Libraries
We are excited to see the Awesome MLOps list growing to almost 600 stars now! Thanks to everyone for your support! This week's edition is focused on industrial strength visualisation frameworks which fall on our Responsible ML Principle #5. The four featured libraries this week are:
  • - An interactive, open source, and browser-based graphing library for Python.
  • Pixiedust - PixieDust is a productivity tool for Python or Scala notebooks, which lets a developer encapsulate business logic into something easy for your customers to consume.
  • ggplot2 - An implementation of the grammar of graphics for python.
  • seaborn - Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
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
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 Conferences
  • AI Conference Beijing [18/06/2019] - O'Reilly's signature applied AI conference in Asia in Beijing, China.
  • Data Natives [21/11/2019] - Data conference in Berlin, Germany.
  • ODSC Europe [19/11/2019] - The Open Data Science Conference in  London, UK.
Business Conferences
  • World Summit AI Americas [10/04/2019] - Large scale AI summit in Montreal, Canada.
    • Come join our panel on AI Ethics and Tools.
  • Big Data LDN 2019 [13/11/2019] - Conference for strategy and tech on big data in London, UK.
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. It seems that the demand for data scientists continues to rise!
Leadership Opportunities
Mid-level Opportunities
Junior Opportunities
© 2018 The Institute for Ethical AI & Machine Learning