The MachineLearning EngineerIssue #187

This #187 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 10,000+ subscribers. 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 #187:

• MLOps Meetup Driverless & E2e
• Stitchfix and their MLOps Platform
• AI Infra Ecosystem Report 2022
• Stanford Transformers Course
• Practical Guide to A/B Testing
• Upcoming MLOps Events
• 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 Meetup Driverless & E2e

The MLOps meetup comes back this week with a fantastic set of seasoned MLOps practitioners sharing insights and best practices. This session Wayve Senior Software Engineer Alex Persin and Contino AI & ML Practice Lead Byron Allen will be diving into best practices and learnings around autonomous vehicles and enterprise production machine learning operaitons.

Stitchfix and their MLOps Platform

Organisations continue to develop internal end-to-end MLOps platforms to service their data science operations at scale. The engineering team at Stitchfix provide an insight into the approach, principles, architectural foundations, adoption and next steps for the development of their internal MLOps platform.

AI Infra Ecosystem Report 2022

The AI Infrastructure alliance has released the 2022 AI Infra Ecosystem report. This report aims to provide team leads, tech executives and architects the key knowledge needed to build and expand producation-grade ML infrastructure.

Stanford Transformers Course

Since their introduction in 2017 transformers have revolutionised Natural Language Processing. Stanford has now published a fantastic free open course that dives into this field, covering conceptual and practical insights on transformers.

Practical Guide to A/B Testing

A/B testing is key in production machine learning systems. This article provides a practical deep dive into the motivations as well as frameworks and techniques that can be leveraged to implement A/B tests at scale in Kubernetes.

Upcoming MLOps Events

The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.

Conferences we’ll be speaking at:

• PyData London - June 17th-19th @ London [ML Acceleration]
• EuroPython - July 11th-17th @ Dublin[ML Security]

Other relevant upcoming MLOps conferences:

• EuroSciPy - August 29 @ Switzerland
• World Summit AI - 12th-13th October @ Neatherlands
• Kubecon NA - 24th-28th October @ Detroit

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 outlined below.

• 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 open source and open community events that are not listed do give us a heads up so we can add them!

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!