The Institute for Ethical AI & Machine Learning

THE ML ENGINEER Issue #11

This week in Issue #12: Bias and explainability in machine learning, federated learning with PyTorch, ultra data visualisation deep dive, practical tutorial on recommenders, intro to learning curves for model evaluation, cybersecurity in development of ML, data optimisation frameworks, new AI conferences, ML jobs and more!   Support the ML Engineer! 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!

Bias and Explainability in ML A deep dive on algorithmic bias and explainability in data and machine learning using the XAI Library. This video provides a case study automating a loan aproval process to show how undesired bias can be introduced throughout the process. The talk also covers how these undesired biases can be tackled using open source tools, together with a three step process consisting of 1) data analysis, 2) model evaluation and 3) production monitoring.

Federated Learning with PyTorch Great practical tutorial to introduce federated learning using the PySift Library to distribute a MINST deep learning task across multiple devices. Federated learning is a method that allows for machine learning models to be trained across multiple edge devices in the network instead on a central server, which we covered a few weeks ago.

Data Visualisation Deep Dive Really awesome free e-book on all-things data visualisation using R. This very comprehensible resource introduces best practices in data analysis, R usage, data transformation, colour/display selection, graph usage, models, maps and more (much more). You can also buy a hard copy in amazon to support the cause.

Practical recommenders tutorial Hands on end-to-end tutorial on recommender systems that dives into the movie dataset. It follows a CRISP-DM-like process to explain every stage in the model developing stage (e.g. data understanding, preparation, etc). The tutorial covers fundamental concepts in recommender systems such as explicit/implicit feedback and dives into practical examples implementing Collaborative Filtering (ALS), Neural Collaborative Filtering, Restricted Boltzman Machine, Smart Adaptive Recommendations, Surprise SVD and Vowpal Wabbit.

Learning Curves in ML evaluation Learning curves are a fundamental technique to evaluate machine learning models. Machine learning mastery brings us a gentle introduction to learning curves to diagnoes machine learning model performance. It provides an introduction to learning curves, an example on how to implement them, and an overview on how to read the graphs to diagnose an underfit, overfit or a well-fit model.

Machine Learning Cybersecurity It is common to hear machine learning applications to cybersecurity, but it's also critical to dive into the cybersecurity applications in machine learning. This great post in the O'Reilly blog provides an overview of the flavours in which vulnerabilities can appear in machine learning model development, together with a few conceptual steps to take into account when taking into consideration machine learning development security.

MLOps = Featured OS Libraries We are excited to see the Awesome MLOps list growing to almost 300 stars now! Thanks to everyone for your support! This week's edition is focused on new libraries on Data Storage Optimisation which fall on our Responsible ML Principle #4. The four featured libraries this week are:   Alluxio - A virtual distributed storage system that bridges the gab between computation frameworks and storage systems. EdgeDB - NoSQL interface for Postgres that allows for object interaction to data stored. BayesDB - Database that allows for built-in non-parametric Bayesian model discovery and queryingi for data on a database-like interface Apache Arrow - In-memory columnar representation of data compatible with Pandas, Hadoop-based systems, etc   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   DataFest19 [11/03/2019] - Two week festival of Data Innovation hosted across Scotland, UK.   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.     Business Conferences   Big Data & AI Tech World [12/03/2019] - AI & Big Data Business conference in London, UK. If you are around, do join our talk on AI Explainability.   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!   Junior Opportunities   Seldon is hiring for a Machine Learning / Data Engineer in London Atlas ML is hiring for a Machine Learning / NLP 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 McLaren Racing is hiring for a Machine Learning Engineer 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 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   Leadership Opportunities 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

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