The Institute for Ethical AI & Machine Learning

THE ML ENGINEER 🤖 Issue #38

This week in Issue #38:   Continuous delivery for machine learning What is AIOps and why you should care Language models as knowledge bases AWS Data Security best practices A smooth approach to production ML ML Explainability libraries 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!

Continuous Delivery for ML Great article from martinfowler.com tackling the challenge of Automating the end-to-end lifecycle of Machine Learning applications. As we have seen, the process for developing, deploying, and continuously improving them is more complex compared to more traditional software. This article proposes Continuous Delivery for Machine Learning (CD4ML), which is explained as the discipline of bringing Continuous Delivery principles and practices to Machine Learning applications.

AIOps and why you should care The first of a two-part series on the emerging concept of AIOps. Whilst a lot of articles that we reference focus on the productionisation techniques and hence "DevOps for ML", the keyword of AIOps is growingly used to refer to applying ML to DevOps. Key examples of this would be to leverage concept drif, outlier detection, explanations and beyond. Although there is still ambiguity with these terms and concepts, here the new stack provides a deep dive on these increasingly discussed topics.

Lang models as knowledge bases Interesting perspective on how the potential of languge models can be generalised. In this paper, the authors argue that language models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as "fill-in-the-blank" cloze statements. They suggest that language models have advantages over structured knowledge bases, and they propose the "LAMA (LAnguage Model Anal-ysis) probe" to test the factual and commonsenseknowledge in language models. They have also made their code available open source.

AWS data security best practices In AI data is key, and with growing usecases it's key to ensure your security is aligned with best practices. Great article that outlines (as more of a reminder) a set of simple principles to follow as best practices when handling data in AWS (although it could also apply to other clouds).

A smooth approach to prod ML Another great artcile that dives into the challenges of dealing with production machine learning system. In this article there are multiple areas discussed, including a dissection of the "lambda architecture", online learning, and a few other topics. The article also shares some lessons learned, and covers some hands on examples using a really interesting library called "creme".

OSS: Explainability Libraries   The theme for this week's featured ML libraries is ML Explainability, and we're happy to share brand new libraries into that section. The four featured libraries this week are:   tensorflow's lucid - Lucid is a collection of infrastructure and tools for research in neural network interpretability. rationale - Code to implement learning rationales behind predictions with code for paper "Rationalizing Neural Predictions" anchor - Code for the paper "High precision model agnostic explanations", a model-agnostic system that explains the behaviour of complex models with high-precision rules called anchors. woe - Tools for WoE Transformation mostly used in ScoreCard Model for credit rating   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     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.   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.   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 Seldon is hiring for a Senior Machine Learning Engineer in London 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   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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