The MachineLearning EngineerIssue #177

This #177 edition of the ML Engineer newsletter contains curated articles, tutorials and blog posts from experienced Machine Learning and MLOps professionals. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀

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This week in Issue #177:

• MLOps London Meetup May
• End-to-end Open Source MLOps
• Machine Learning at Discord
• Faceboook’s 175B Model Release
• Running Kubernetes in Production
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

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 London Meetup May

The MLOps meetup comes back this week with key insights in the MLOps space, this time the sessions will dive into production grade feature-stores, as well as best practices for machine learning security at scale.

End-to-end Open Source MLOps

A fantastic practical deep dive into building an end to end MLOps pipeline, covering the various phases of the machine learning lifecycle using a broad range of machine learning libraries. This article covers the conceptual, architectural and practical aspects.

Machine Learning at Discord

The Data Exchange podcast comes back this week with an insightful conversation with Discord ML Leader Gaurav Chakravorty on building industrial grade machine learning systems for search, recommenders, and personalization systems.

Faceboook’s 175B Model Release

The AI team at Facebook has provided access to large-scale language models, sharing their release of the Open Pretrained Transformer (OPT) 175 Billion parameter model, as well as some of their plans to continue contributing to open research resources.

Running Kubernetes in Production

A great practical summary of key important areas to consider when running Kubernetes in production, as this tool becomes more ubiquitous in the MLOps space it’s always key to ensure runtime infra is following best practice.

Open Source MLOps Tools

Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. 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. Four featured libraries in the GPU acceleration space 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!