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THE ML ENGINEER Issue #8     The ML Engineer newsletter has reached over 500 subscribers, and the Awesome MLOps list almost 200 stars! Thank you so much for everyone's support!   This week in Issue #8: Papers with code brings a new update, NVIDIA AI generates simulations, new research analysing over 16000 papers, insights on the tensorflow 2.0 APIs, limitations of serverless in machine learning, google brain research in 2018, libraries on research notebooks, upcoming ML conferences and machine learning jobs!   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!         Papers with code update The AtlasML team brings on a great update on their popular sevice "Papers with code". This time they bring an impressive update with over 950+ ML tasks, 500+ evaluation tables (including state of the art results) and 8500+ papers with code. This is definitely one to watch in 2019.       NVIDIA's AI generated graphics The NVIDIA team brings again yet another mind-boggling piece of research. This time, they have created the first video game demo using AI-generated graphics. In the acompanying video they show how they built a demo which was rendered by a deep neural network as opposed to a graphics engine.     Serverless and machine learning "Serverless Computing: One Step Forward, Two Steps Back" is a fascinating research paper by several UC Berkeley researchers. They take a bold step into identifying areas where Serverless computing still needs to improve. The paper discusses key points such as function lifetime limits, network reliance, slow IO writes, lack of specialised hardware, etc. The paper also explores 3 case studies to highlight some of these issues, which include: 1) training machine learning models, 2) low latency prediction serving, and 3) a leader election protocol.       Tensorflow 2.0 APIs A brief post that delves into the Tensorflow 2.0 APIs and shows the tradeoffs when chosing to use the Keras Sequential/Functional API vs the Keras Subclassing API. It is great to see that the tensorflow team is bringing together all the separate popular components and compiling them into one cohesive platform.     16k research paper analysis MIT Tech Review downloaded the abstracts of 16,625 papers available in the "artificial intelligence" section through November 18, 2018 and tracked the words mentioned to see how the field has evolved. In the article, MIT Tech Review provides a visual insight on the findings as the neural-network boom enters the research space, together with a brief insight on the next decade.     Google Brain Research in 2018 Very comprehensible article by the Google Brain team that covers the highlights of their research in 2018. In the post they cover areas that include ethical principles, social good uses, assistive technology, quantum computing, NLU (including BERT), computational photography, neural architecture search and more.       MLOps = Featured OS Libraries We are excited to see the Awesome MLOps list growing to almost 200 stars. Thanks to everyone for your support! This week's edition is focused on data science notebook frameworks which fall on our Responsible ML Principles #2, #3, #4 and #5. The four featured libraries this week are: Jupyter Notebooks - Web interface python sandbox environments for reproducible development Stencila - Stencila is a platform for creating, collaborating on, and sharing data driven content. Content that is transparent and reproducible. RMarkdown - The rmarkdown package is a next generation implementation of R Markdown based on Pandoc. H2O Flow - Jupyter notebook-like inteface for H2O to create, save and re-use "flows"   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!        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   Mid-level Opportunities QuantumBlack is hiring for a Senior Machine Learning Engineer in London Proportunity 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 Skyscanner is hiring for a Senior Director of Sciences in London       MLConf = Conferences & Events From this week on, we will be featuring 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   FOSDEM [02/02/2019] - Europe's largest open source conference in Brussels, Belgium. We are speaking on the state of MLOps in 2019 at the HPC, Big Data & Data Science track - come say hello!   PyCon Belarus [15/02/2019] - The 5th edition of the Python conference in Minsk, Belarus. We'll be speaking on Machine Learning Explainability and Bias Evaluation.   Machine Learning Prague [22/02/2019] - A practical conference on machine learning & DL in Prague, CZ.     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 in Business Ethics [09/02/2019] - Conference on AI business and ethics in London, UK. Join our panel on the impact of AI in industry!   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.       © 2018 The Institute for Ethical AI & Machine Learning