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Issue #30
Our "Awesome Production Machine Learning" list has reached over 700 stars and our AI explainability library has reached over 200 🎉 thanks to everyone for your support! Let's continue exploring the challenges and opportunities of production ML 🚀
This week in Issue #30:
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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!
This week we presented at PyData London and EuroPython Basel on produciton-level machine learning model explainers, which is an approach to leverage explanations end-to-end with the purpose to align with higher level frameworks like regulation or industry standards. The slides are available online and include code examples for data analysis with XAI, black box model analysis with Alibi and production explainers with Seldon.
An awesome research paper published in Arxiv this week by Seldon Data Scientists Arnaud Van Looveren and Janis Klaise titled "Interpretable Counterfactual Explanations Guided by Prototypes". This paper dives into the concept of counterfactuals, which is an ML local model explanation technique that allows you to ask the question "for this ML prediction, what could be the smallest changes I could do to the input to change the outcome?". Being such a computationally expensive task, this paper proposas a new approach to reduce the computational resources required to use this technique.
One of the most interesting white papers so far, written by Immuta's Chief Privacy Officer Andrew Burt. This paper covers critical topics on privacy and cybersecurity, as well as how these topics have been changing as we move into massive scale production systems. This paper also provides great historical case studies that provide an insight of how important conceptual shifts and standardisation of thses concepts will be.
O'Reilly's Ben Webb brings us a great high level overview of some of the key keynotes at the AI O'Reilly Beijing. Some of these include the future of hiring in AI, RISELab innovations, breakthroughs, data orchestration, AI in retail, data structures and more.
Great post from Machine Learning Mastery that dives into the more practical side of GANs. This article covers use-cases of GANs across various datasets of image and text types.
OSS: Functions-as-a-service
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
  • KNative - Serverless framework built on Kubernetes
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
  • 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
  • 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