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Issue #40
This week in Issue #40:
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The topic of cybersecurity in machine learning has seen an increase in activity in the community due to its critical nature around production systems. This blog post covers a fascinating competition that took place at DEFCON, where participants were tasked with tricking a ML classifier trained to detect malware. In this article the author provides an insight on how the challenge was applied together with the techniques used to succeed.
Production machine learning systems have proven that the nuanced challenges that are faced when deploying machine learning require a new paradigm. O'Reilly's Mike Loukides does a fantastic job in his latest article to provide an overview to the topic of machine learning deployment, together with insights on how this challenge is currently being tackled.
Although most of the carefully curated datasets that you may come across online may be on relational or key-value store, there has been an ever-increasing interest on graph datasets, as most of the data we interact with on a regular basis will tend to have more complex, and often grap-like structures. This article provides a comprehensible and non-exhaustive list of graph algorithms to get acquaintanced with - these include an intuitive explanation, insights of where they may be relevant and an example code implementation.
As larger and more critical datasets (and decisions) become part of the machine learning end-to-end production workflow, the challenges with statistical and societal bias/fairness become more complex. This survey provides a very comprehensible deep dive on the concepts and taxonomies around the concepts of "types of bias", "types of discrimination", and "types of fairness", together with how these interact with the different types of machine learning techniques.
The current implications of data privacy and trust has led into reviving interest into extremely fascinating research areas that have existed for decades. This one in particular is differential privacy, a technique that allows for data to be anonymised in a way that still leaves statistical properties which allow for processing on top of the anonymised data, which can lead to improvements in privacy. Google has released a C++ library of ε-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information.
The theme for this week's featured ML libraries is Privacy Preserving Machine Learning libraries, and we're happy to share brand new libraries into that section. The four featured libraries this week are:
  • Intel Homomorphic Encryption Backend - The Intel HE transformer for nGraph is a Homomorphic Encryption (HE) backend to the Intel nGraph Compiler, Intel's graph compiler for Artificial Neural Networks
  • PySyft - A Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) within PyTorch
  • Microsoft SEAL - Microsoft SEAL is an easy-to-use open-source (MIT licensed) homomorphic encryption library developed by the Cryptography Research group at Microsoft
  • Tensorflow Privacy - A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy
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
© 2018 The Institute for Ethical AI & Machine Learning