The MachineLearning EngineerIssue #83

This week in Issue #83:

• Building an Enterprise Deep Learning Stack
• 5 Key Features for ML Platforms
• AI Dungeon Open World w GPT3
• The State of Apache Airflow
• D2IQ KUDO for Kubeflow
• [Updated] AI Conferences Gone Virtual in 2020
• 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!

Building an Enterprise DL Stack

Determined AI has put together a fantastic article outlining how they leveraged open source and enterprise tools to build an end to end deep learning platform. They cover some of the motivations that lead to require end to end capabilities, dive into some of the key challenges, and provide a solution for each phase of the model lifecycle.

5 Key Features for ML Platforms

ML Platform Designers need to meet current challenges and plan for future workloads. In this post by Anyscale Ben Lorica and Ion Stoica cover some of the key components in the machine learning lifecycle, as well as how the different components of Ray tackle each of these pieces, including model training, model tuning, model serving and model monitoring.

AI Dungeon Open World w GPT3

This week there has been a large surge of GPT3 case-studies showcasing the astonishing capabilities of this massive-scale new model. AI Dungeon has been an early adopted for the GPT-x algorithms, and has included a release to their open world, proceduraly generated, smart AI text-based adventure game.

The State of Apache Airflow

Apache Airflow creator Maxime Beuchemin joins the Software Engineering Daily podcast to dive into the state of Airflow in 2020. Since Airflow’s creation, it has powered the data infrastructure at companies like AirBnb, Netflix, Lyft and beyond. It has had a huge, and growing impact in the data pipeline space, and there’s a lot yet to come.

D2IQ KUDO for Kubeflow

D2IQ (formerly known as Mesosphere) has announced their new machine learning platform KUDO, which builds on top of the Kubeflow project at scale. This end-to-end platform allows showcases the power of open source, largely through the adoption of the Kubeflow framework, which has continued to grow in features and impact, bringing machine learning into the Cloud Native / Kubernetes ecosystem at massive scale.

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.