The MachineLearning EngineerIssue #10

This week in Issue #10:

The delayed impact of FAIR Machine Learning, getting started with Google Collab, Data Science salaries in Europe, building the tensorflow API, OpenAI’s language model, distributed AI in the blockchain, scaling your machine learning and more!

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Delayed impact of FAIR ML

Berkeley researchers bring us an incredibly interesting paper (which also won one of the “best paper awards” at ICML 2018) that discusses the delayed impact of “introducing fairness” into machine learning models. The accompanying blog post provides a very comprehensible breakdown of the approaches towards “fairness” as well as interactive graphs that showcase the impact of thresholds on metrics such as profit and credit score change.

Getting started with Google Collab

Google collab is an awesome service. This post provides a brief introduction to this free and fully-managed “google-docs meets jupyter notebook” service provided by google to make it easy to experiment and collaborate. A few weeks ago we covered alternative open source notebook frameworks, which we also recommend to check out as there are quite a lot of awesome tools out there!

Data Science Salaries in Europe

This post in data-economy puts numbers into the data science hype, and provides an insight on what the demand actually looks like. In brief, [spoiler alert] Switzerland offers the highest Data Scientist salaries in Europe and Python is the top production coding language for Data Scientists (sorry R).

Building tensorflow from scratch

What a better way to understand TensorFlow than by mimicking its API from scratch. In this post, they do just that. The article starts by introducing some fundamental concepts from Tensorflow such as computational graphs, placeholders, variables, etc. It then dives into code examples re-creating some of the APIs of these fundamental structures. By the end of the post, you will have successfully implemented some core APIs from TensorFlow 👏.

Better language models

OpenAI took the world by surprise with a very interesting piece of research released this week. The team basically trained a language model using a successor to GPT, trained to predict “the next word” in 40GB of internet text. OpenAI decided not to release the trained model due to concerns about malitious applications, which triggered several over-hyped articles in the mainstream media.

AI where? In the blockchain 💰

A service called SingularityNET, a “decentralized artificial intelligence marketplace” has launched a BETA version of their service. In this post, one of their marketing executives provides an overview of their platform, together with core functionalities and concepts, as well as steps to get started creating an “AI service”.

MLOps = Featured OS Libraries

We are excited to see the Awesome MLOps list growing to almost 300 stars now! Thanks to everyone for your support! This week’s edition is focused on new libraries on Explainability and Bias Evaluation which fall on our Responsible ML Principles #2 and #3. The four featured libraries this week are:

• DeepLIFT - Codebase that contains the methods in the paper “Learning important features through propagating activation differences”. Here is the slides and the video of the 15 minute talk given at ICML.
• Skater - Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases.
• Tensorflow’s cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both. A python library to benchmark system’s vulnerability to adversarial examples.
• XAI (eXplainableAI) - An eXplainability toolbox for machine learning that provides a process for data analysis, model evaluation and production monitoring based on the AI Procurement Framework.

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!

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. It seems that the demand for data scientists continues to rise!

Junior Opportunities

• Seldon is hiring for a Machine Learning / Data Engineer in London
• Atlas ML is hiring for a Machine Learning / NLP Engineer in London
• Migacore is hiring for a Machine Learning Engineer in London
• CloudNC is hiring for a Machine Learning Engineer in London
• Babylon Health is hiring for a Machine Learning Engineer in London

Mid-level Opportunities

• QuantumBlack is hiring for a Senior Machine Learning Engineer in London
• Proportunity is hiring for a Senior Machine Learning Engineer in London
• Twitter is hiring for a Senior Machine Learning Engineer in London
• StreetBees is hiring for a Senior Data Scientist in London
• Apple is hiring for a Senior Machine Learning Engineer in London
• Expedia is hiring for a Principal Data Scientist in London

Leadership Opportunities

• Fractal Labs is hiring for a VP of Engineering in London
• Distributed is hiring for a VP of Engineering in London
• FactMata is hiring for a Head of Machine Learning in London
• Skyscanner is hiring for a Senior Director of Sciences in London

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 Conferences

• Machine Learning Prague [22/02/2019] - A practical conference on machine learning & DL in Prague, CZ.

• PyCon + PyData Florence [02/05/2019] - Python X comes this year with a PyData focus in Florence, Italy.

• AI Conference Beijing [18/06/2019] - O’Reilly’s signature applied AI conference in Asia in Beijing, China.

Business Conferences

• Big Data & AI Tech World [12/03/2019] - AI & Big Data Business conference in London, UK.

• If you are around, do join our talk on AI Explainability.

• AI Expo Global [19/04/2019] - Global conference on artificial intelligence in London, UK.

• Come join us at our talk on AI orchestration at scale.