The MachineLearning EngineerIssue #84

This week in Issue #84:

• Netflix On Cutting Data Costs
• Awesome Github GPT-3 List
• Lessons from ML Prod Monitoring
• Uber on Editing Massive GeoData
• Airflow Summit Videos are Out
• Featured OSS Production ML Libraries
• Awesome AI Guidelines to check out this week
• + more 🚀

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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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone’s support!

Netflix On Cutting Data Costs

Netflix has put together an article that outlines their approach to making their data infrastructure cost effective, whilst providing transparency and efficiency across the organiastion. They cover their motivations, dataflow, tools, learnings and future focus.

Awesome Github GPT-3 List

Recently there has been a surge of mind blowing AI prototypes built on top of the GPT-3 API, this awesome github repo has a compiled list of some of the available prototypes, categorised across apps, search, codegen, general reasoning and other interesting areas.

Lessons from ML Prod Monitoring

ML in Production has released a multi-part series sharing insights obtained around monitoring of machine learning systems in production. In this article they dive into 3 key lessons together with key takeaways.

Airflow Summit Videos are Out

The Apache Airflow Summit took place recently, and the videos for the conference have just been released. These include a broad set of deep dives, keynotes, case studies and specialised use-cases - over 35 talks available for free streaming.

Uber on Editing Massive GeoData

Uber is known for dealing with geospatial data, and recently they have released an article outlining their approach towards editing massive geospatial datasets with their open source project Nebula.GL, a toolset designed for performant geometry editing in a web browser with massive geospatial datasets.

OSS: Model Serving Framework

The topic for this week’s featured production machine learning libraries is Model Serving Frameworks. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are:

• KFServing - Serverless framework to deploy machine learning models in Kubernetes with KNative
• Seldon Core - Open source platform for deploying and monitoring models in kubernetes with rich DAG structures
• Cortex - Cortex is an open source platform for deploying machine learning models—trained with nearly any framework—as production web services.
• Tensorflow Serving - High-performant framework to serve Tensorflow models via grpc protocol able to handle 100k requests per second per core

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!

OSS: Awesome AI Guidelines

As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle thiese challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. We will be showcasingitg three resources from our list so we can check them out every week. This week’s resources are:

• AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems

• An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles

• ACM’s Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018

• From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices

If you know of any guidelines that are not in the “Awesome AI Guidelines” list, please do give us a heads up or feel free to add a pull request!

About us

The Institute for Ethical AI & Machine Learning is a Europe-based research centre that carries out world-class research into responsible machine learning.