The MachineLearning EngineerIssue #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 🚀

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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!

Best practices for ML Engineering

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.

The Journal of Open Source

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.

Visualising Attention in Deep NLP

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.

Pruning API in Tensorflow

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.

ML Explanations with VIBI

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.

Two missing links in serverless

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:

• Plotly.py - 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!

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

• 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.

• RAAIS 2019 [28/06/2019] - The Research and Applied AI Summit in London, UK

• Data Natives [21/11/2019] - Data conference in Berlin, Germany.

• ODSC Europe [19/11/2019] - The Open Data Science Conference in London, UK.

• Spacy IRL [05/07/2019] - SpaCy NLP’s First F2F Conference in Berlin, Germany.

• EurNLP [11/10/2019] - Europe’s NLP research conference (pronounced “Your NLP”) in London, UK

• Khipu AI [11/11/2019] - Latin American Meeting in Artifical Intelligence in Montevideo, Uruguay.

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.

• 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.

• Predictive Analytics World [18/11/2019] - Conference for Business AI in Berlin, Germany.

• Big Data LDN 2019 [13/11/2019] - Conference for strategy and tech on big data in London, UK.

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!

Leadership Opportunities

• Algorithmia is hiring for a VP of Engineering in Seatle, USA
• 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
• Brainpool.ai is hiring for a Head of Machine Learning in London, UK
• Cytora is hiring for a Data Science Director in London

Mid-level Opportunities

• Proportunity is hiring for a Senior Machine Learning Engineer in London
• Twitter is hiring for a Senior Machine Learning Engineer in London
• Atlas ML is hiring for a Lead NLP Engineer in London
• StreetBees is hiring for a Senior Data Scientist in London
• Expedia is hiring for a Principal Data Scientist in London
• QuantumBlack is hiring for a Senior Machine Learning Engineer in London
• Tractable is hiring for a Senior Deep Learning Engineer

Junior Opportunities

• Seldon is hiring for a Machine Learning / Data 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
• Chattermill is hiring for a Machine Learning Engineer in London