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Issue #37
This week in Issue #37:
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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
The need for real time machine learning use-cases in production is increasing. This talk provides practical insight on how to build real time data streaming machine learning pipelines that are production ready, covering a case study performing automated content moderation on Reddit comments in real time. The talk dives into fundamental concepts of stream processing such as windows, watermarking and checkponting, and show how to use frameworks like Kafka, Spacy and Spark Streaming.
O'Reilly comes with an awesome podcast, talking with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.
Great overview on the concept, techniques and key areas in machine learning interpretability. This article covers several classes of interpretability methods such as model-specific vs model-agnostic, techniques such as partial dependency plots, permutation importance, anchors, and more,
Notebooks have rapidly grown in popularity among data scientists to become the de facto standard for quick prototyping and exploratory analysis.This post provides a very interesting insight on the infrastructure and processes that Neflix has introduced internally around Notebooks and data. This article covers the processes that involve the roles of analysts, data scientists and data engineers, as well as the challenges with data access, templates and infrastructure.
How AI Solves the Kubernetes Complexity Conundrum. A really interesting article from the new stack that dives into the challenges and complexities that the introduction of kubernetes has brought to the tech world, and makes a high level case on how AI will help manage some of the key complexities that the scale of kubernates-based systems will entail.
The theme for this week's featured ML libraries is Industry-strength NLP, and we're happy to share brand new libraries into that section. The four featured libraries this week are:
  • Snorkel - Snorkel is a system for quickly generating training data with weak supervision.
  • GluonNLP - GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your NLP research.
  • OpenAI GPT-2 - OpenAI's code from their paper "Language Models are Unsupervised Multitask Learners".
  • Facebook's XLM - PyTorch original implementation of Cross-lingual Language Model Pretraining which includes BERT, XLM, NMT, XNLI, PKM, 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
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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