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Issue #206
We are EXCITED for NeurIPS 2022 coming up next week 🤩 Join us next week on Friday at the Workshop on "Challenges in Deploying & Monitoring ML Systems" where we'll give a Keynote on Security in Prod ML 🚀 We'll also be participating in a panel this week on Algorithmic Responsibility with the ACM, as well as presenting TWO Talks at the PyData Global 2022 💫 We hope to see you there in one of those!
This #206 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 15,000+  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 the MLE #206:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
The PyData Conference 2022 is coming up this week with exciting keynotes & sessions from RStudio Chief Scientist to Github CEO. We are thrilled to be contributing with two fantastic talks this Friday on "Industry Strength DALL-E / SD" and "Metadata Systems for E2e ML Systems". Come join us virtually this week!
Machine learning is increasingly being used globally by governments and companies to make or recommend decisions that have far-reaching effects on individuals, organizations, and society. Join us this Thursday on this panel where we'll be discussing our recently published ACM Policy Principles on Algorithmic Responsibility.
The EU expects an annual cost reduction of €180b - €290b from their recent initiative to revamp their cybersecurity regulation. Production machine learning systems are a critical part of the infrastructure to secure. The European Union Agency for Cyber published a report exploring the securing of machine learning algorithms.
Standford's Center for Research on Foundation Models has published a fantastic analysis of thirty well-known large language models, compared against various metrics including accuracy, robustness, fariness, toxicity, bias and efficiency. This is a fantastic initiative as it provides a step towards standardised evaluation and assessment of this fast-evolving area of large foundational models.
The text-book on Structure and Interpretation of Computer Programs, more popularly known as SICP is a key resource for practitioners looking to build a strong foundation in computer science. This MIT Open Courseware provides an in-depth accompanying resource for anyone looking to dive into (or review) this fantastic book; thorowly recommended for MLE practitioners.
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:
Other relevant upcoming MLOps conferences:
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!
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:
  • MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
  • 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.
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!
© 2018 The Institute for Ethical AI & Machine Learning