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Issue #38
This week in Issue #38:
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Great article from 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.
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.
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.
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).
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".
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
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
  • 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
  • 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.
Leadership Opportunities
Mid-level Opportunities
Junior Opportunities
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