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Issue #22
This week in Issue #22:
A conversation on practical NLP, ML reproducibility infrastructure, Berkeley on Serverless, Face detection in Python, Human-Centric ML Infrastructure, tutorial on CNNs, lambda frameworks, upcoming ML conferences, data science / ML engineering jobs and more 🚀.
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A great conversation with SpaCy co-creator Ines Montani in the "This week in ML (TWiML)" podcast. In this session, Ines dives into the challenges and trends within the world of NLP, as well as the need for "industry-ready" tools in the NLP space that provide more than just research capabilities. During this podcast they also cover really interesting areas, such as the word vector algorithm they created, which they named Language Modelling with Approximate Outputs (aka LMAO).
Really insightful introduction to the need for reproducibility infrastructure at the ICLR2019. This presentation covers some of the tools and techniques to tackle this challenge of reproducibility, as well as an insight on several other challenges that are starting to appear in the ML space such as explainability. The video for 2018 is available as well as the slides for 2019.
After Berkeley's successful report a few years back that demystified the concept of cloud computing, they have put together a new report that aims to do the same for serverless technologies. They provide a high level framework to differentiate normal cloud computing with serverless with three key characteristics:1) Decoupling of computation and storage; they scale separately and are priced independently. 2) The abstraction of executing a piece of code instead of allocating resources on which to execute that code. 3) Paying for the code execution instead of paying for resources you have allocated to executing the code.
A great hands on tutorial from Towards Data Science covering core fundamentals in computer vision as well as hands on examples, which will allow you to have a functional face detection algorithm by the end. The post covers face detection with Haar Cascade Classifiers using OpenCV, Histogram of Oriented Gradients using Dlib and Convolutional Neural Networks.
Great deep dive by Ville Tulus from Netflix, providing key knowledge on MLOps core concepts, as well as the needs for ML operations frameworks. Ville also introduces a tool that is being used internally within Neflix to manage their machine learning infrastructure.
The MNIST handwritten digit classification problem is a standard dataset used in computer vision and deep learning. Machine learning mastery provides a hands on tutorial that shows how to tackle this challenge using convolutional neural networks.
MLOps = Featured OS Libraries
This week's edition is focused on new libraries on Function as a Service Frameworks which fall on our Responsible ML Principle #4. The four featured libraries this week are:
  • OpenFaaS - Serverless functions framework with RESTful API on Kubernetes
  • Fission - Serverless functions as a service framework on Kubernetes
  • Hydrosphere ML Lambda - Open source model management cluster for deploying, serving and monitoring machine learning models and ad-hoc algorithms with a FaaS architecture
  • Hydrosphere Mist - Serverless proxy for Apache Spark clusters
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 Conferences
  • AI Conference Beijing [18/06/2019] - O'Reilly's signature applied AI conference in Asia in Beijing, China.
  • 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
  • World Summit AI Americas [10/04/2019] - Large scale AI summit in Montreal, Canada.
    • Come join our panel on AI Ethics and Tools.
  • 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. It seems that the demand for data scientists continues to rise!
Leadership Opportunities
Mid-level Opportunities
Junior Opportunities
© 2018 The Institute for Ethical AI & Machine Learning