Subscribe to the Machine Learning Engineer Newsletter

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

Issue #180
This #180 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 #180:
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!
Feature stores have become a growingly popular topic in the MLOps space. Netflix engineering provides great insights on how they have built and evolved their feature store engines, together with architectural deep dives as well as lessons learned.
MLOps can be overwhelming; especially due to the sheer number of tools available in every stage of the ML model lifecycle. This  initiative addresses this by providing a webapp to allow practitioners to experiment with different architectures considering different tools for their e2e MLOps requirements interactively.
How to Avoid Pitfalls of MLOps
The MLOps journey is full of risks and pitfalls due to being an emerging field where best practices are still being defined. The ZenML team has put together a great overview of some of these challenges as well as best practices, tooling and their approach on solving it with the ZenML framework.
We have all been blown away by the unexpectedly creative capabilities of the text-to-image DALL-E model. We are now starting to see a trend with Google Brain's release of the Imagen model, which suggests an unprecendented degree of photorealism, and delivers mind blowing results with humorous pictures such as "a panda riding a skateboard...".
Recommender systems are fundamental to major internet companies. This resource is an insightful applied research effort from the Airbnb team showcasing how they were able to approach real-time and historical recommendations for listings by abstracting and applying the concept of embeddings into their data-usecases.
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