The Institute for Ethical AI & Machine Learning

THE ML ENGINEER 🤖 Issue #28

This week in Issue #28:   The state of AI in 2019 Production machine learning in 2019 Model governance and operations The best of modern NLP Adversarial examples with FGSM AI conferences ML jobs + more 🚀   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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!

The state of AI in 2019 Nathan Benaich and Ian Hogarth bring us the report outlining the state of AI in 2019, which aims to provide an overview on the current state and advances in 5 key areas: research, talent, industry, china and politics. This annual report covers the most interesting things that they have seen in the last 12 years. You can also find last year's report here.

Production ML in 2019 During Kubecon Shanghai 2019 we presented a high level overview of the themes that are growingly becoming critical in the world of production machine learning. This includes over 10 themes which expand over explainability, privacy, model versioning, adversarial robustness and beyond. This repository contains a set of slides that dive into three key themes: black box explainability, model versioning and ML orchestration. For each of these themes, there is a high level explanation, together with a hands on example with a Jupyter notebook, including an end-to-end NLP pipeline, tabular explainers and a pytorchhub integration.

Model governance and ops Machine learning at scale introduces new challenges, as managing a large number of models that perform increasingly critical tasks becomes more complex. O'Reilly Chief Scientist Ben Lorica has put together a great overview of the ecosystem and tools available in the machine learning governance and operations world.

The best of modern NLP Hugging face scientist Victor Sanh has put together an extensive list of resources related to key themes in NLP, including transfer learning, representation learning, neural dialogue, as well as other miscellaneous pieces of research that have contributed to the growth of the field in the last two years.

Adversarial examples with FGSM Tensorflow tutorials has launched a new deep dive on adversarial examples, which covers the conceptual, theoretical and practical aspect of this topic. It provides the code for you to find an adversarial example which could trick a classifier.

OSS: Adversarial Robustness The theme for this week's featured ML libraries is Adversarial Robustness, which includes tools for adversarial attacks and adversarial security. These libraries are an incredibly exciting addition that fall in our Responsible ML Principle #8, and the whole section was contributed by one of the Fellows at the Institute Ilja Moisejevs from Calipso AI. The four featured libraries this week are:   CleverHans - library for testing adversarial attacks / defenses maintained by some of the most important names in adversarial ML, namely Ian Goodfellow (ex-Google Brain, now Apple) and Nicolas Papernot (Google Brain). Comes with some nice tutorials! Foolbox - second biggest adversarial library. Has an even longer list of attacks - but no defenses or evaluation metrics. Geared more towards computer vision. Code easier to understand / modify than ART - also better for exploring blackbox attacks on surrogate models. IBM Adversarial Robustness Toolbox (ART) - at the time of writing this is the most complete off-the-shelf resource for testing adversarial attacks and defenses. It includes a library of 15 attacks, 10 empirical defenses, and some nice evaluation metrics. Neural networks only. AdvBox - generate adversarial examples from the command line with 0 coding using PaddlePaddle, PyTorch, Caffe2, MxNet, Keras, and TensorFlow. Includes 10 attacks and also 6 defenses. Used to implement StealthTshirt at DEFCON!     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   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   EURNLP 2019 [11/10/2019] - European NLP Research 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   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   Mid-level Opportunities Proportunity 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 Tractable is hiring for a Senior Deep Learning Engineer  FactMata is hiring for a Lead Machine Learning Engineer in London   Junior Opportunities   Migacore is hiring for a Machine Learning Engineer in London Babylon Health is hiring for a Machine Learning Engineer in London

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