The MachineLearning EngineerIssue #150

This week in Issue #150:

• MLOps and Devops around Data
• The Guide to GitOps Architecture
• Case Study for Ethics in AI/ML
• Large Scale ML Multi-Modal Data
• ML Monitoring at EuroPython
• 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 and Devops around Data

This article attempts to identify the key principles that distinguish MLOps from DevOps, particularly covering data, compute, orchestration and the software development layers.

The Guide to GitOps Architecture

Weaveworks has released a resource that would be very relevant for MLOps practitioners which provides practical insights around the concept of GitOps, including comparisons with similar concepts, as well as principles, processes and tools.

Case Study for Ethics in AI/ML

The Institute for Ethical AI published a collaboration with NumFocus on how they use foundation tools to enable accountability and ethics in AI and machine learning, which is one of several fantastic case studies including the story of the first photograph of the black hole https://numfocus.org/case-studies.

Large Scale ML Multi-Modal Data

The data exchange podcast dives into conversation with Mist Systems CTO Bob Friday, where they delve into large scale machine learning on multi-modal data.

ML Monitoring at EuroPython

The Production Machine Learning Montioring EuroPython talk is now out, covering standard microservice monitoring techniques applied into deployed machine learning models, as well as more advanced paradigms to monitor machine learning models with Python leveraging advanced monitoring concepts such as concept drift, outlier detector and explainability.

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!

About us

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