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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!
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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.
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 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.
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.
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.
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.
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
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
Mid-level Opportunities
Leadership Opportunities
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
Business Conferences
© 2018 The Institute for Ethical AI & Machine Learning