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Issue #36
This week in Issue #36:
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Federated learning involves training machine learning models distributed over remote devices or siloed data centers (this could be mobile phones or even hospitals, while keeping data localized). Training in heterogeneous and potentially massive networks introduces a lot of new challenges. This great article dives into the unique characteristics and challenges of federated learning, together with current approaches and future work.
It’s easy and fun to ship a prototype, whether that’s in software or data science. What’s much, much harder is making it resilient, reliable, scalable, fast, and secure. This article brings some of the best practices identified by the team at Ravelin. Their data science guidelines include:: 1) all starters will build, train and deploy production models within a week, 2) leverage humans whilst automating manual work, 3) deploy models incrementally and often, 4) end users will never notice a model change other than improved results.
Sequoia has put together a great overview of the career progression of a data scientist - specifically they examine what characteristics senior product data scientists have relative to junior ones, and why a healthy data-informed company should invest in the development of their data scientists. The article covers the key "five core skills" of a data scientist, how data scientists advance, common questions in data science, and key takeaways.
Incredibly insightful deep dive into the topic of observability, which discusses terminology, challenges and key insights. It emphasises that metrics do not equal observability, and provides key terms such as cardinality for system insights, and covers some of the present and future of this very important topic.
Transformers are popular (and effective) sequence-to-sequence models used for language modeling, machine translation, image captioning and text generation. This article covers key concepts, including RNNs, LSTMs, attention, self-attention and then cover how these all fit togethers in the transfoerm architecture.
The theme for this week's featured ML libraries is Industry-strength NLP, and we're happy to announce that we have added over 10 new libraries to the section. The four featured libraries this week are:
  • SpaCy - Industrial-strength natural language processing library built with python and cython by the team.
  • Flair - Simple framework for state-of-the-art NLP developed by Zalando which builds directly on PyTorch.
  • Wav2Letter++ - A speech to text system developed by Facebook's FAIR teams.
  • GNES - Generic Neural Elastic Search is a cloud-native semantic search system based on deep neural networks.
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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