Subscribe to the Machine Learning Engineer Newsletter

Receive curated articles, tutorials and blog posts from experienced Machine Learning professionals.

Issue #181
This #181 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 🚀
If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter,  💼 Linkedin and  📕 Facebook!
This week in Issue #181:
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 rise of MLSecOps is helping identify critial security vulnerabilities at every stage of the MLOps lifecycle, and defining the best practices to mitigate security risks in the different machine learning data & model stages. For anyone interested on a deeper dive on MLSecOps you can check our ongoing initiative to create "Flawed ML Security" examples (and solutions).
The DataOps & MLOps space continues to bring exciting trends that are driving forward some of the most exciting challenges in applied AI in industry. The GradientFlow team has put together a fantastic forward-looking exploration on the key trends in the DataOps & MLOps space, and covers the areas of AutoML, Data-Centric AI, Data Stacks, AI Efficiency, and more.
The future of observability is bright; the 1000+ Splunk 2022 observability survey respondants provide interesting insight such as the reported downtime costs reductions by 90% and improved mean-time-to-resolution by 69%, between many other interesting insights.
Featurestores continue growing in popularity and adoption across production MLOps stacks. As these data-intensive components are used at larger-scale, these have uncovered architectural and conceptual challenges that have required new concepts and architectural patterns. This article provides an interesting introduction to some of these challenges, as well as proposed concepts that enable for online and offline ML features.
Data science is not just a science, and more often than not storytelling is key. The Shopify data science & engineering team has put together an insightful overview of the importance of storytelling in the field of data and data science, as well as a high level conceptual framework to present data insights with impactful results.
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:
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
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:
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