The MachineLearning EngineerIssue #145

This week in Issue #145:

• MLOps LDN Online & In-Person
• First GPU Acceleration Kompute
• ICML 2021 Videos & Slides
• Graph Database Ecosystem
• Kubernetes Operators Made Easy
• Open Source ML Frameworks
• 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!

MLOps LDN Online & In-Person

This coming week the MLOps London meetup is kicking off with their first event aimed at bringing together practitioners that work with production machine learning, and will be taking place both online and in-person.

First GPU Acceleration Kompute

The monthly GPU Acceleration Kompute sessions will kick off with its first one this week. This week’s session will cover GPU acceleration across edge device, convolutional neural network (CNN) implementations, features from v0.8.0 release, optimizations and more.

ICML 2021 Videos & Slides

The Thirty-eighth International Conference on Machine Learning has now made available the slides, videos and resources for the tutorials that took place at the conference, covering a broad range of interesting topics in the machine learning research ecosystem.

Graph Database Ecosystem

The Data Exchange Podcast delves into conversation with O’Reilly’s Paco Nathan on recent trends in large-scale graph technologies, novel applications of graph databases, and general trends that are causing the rise in interest for graph-based data systems.

Kubernetes Operators Made Easy

Kubernetes Custom Resource Definitions were introduced as a cloud-native architectural pattern that has seen growing adoption to enable for reliable distributed systems applications. This article will provide MLOps practitioners with the required intuition to get started.

OSS: GPU Accel. Frameworks

The topic for this week’s featured production machine learning libraries is GPU Acceleration 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:

• Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
• CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
• Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
• CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.

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 these 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. You can find multiple principles in the repo - some examples include the following:

• 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!