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Issue #184
This #184 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 #184:
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 top machine learning course has historically been Andrew Ng's Stanford course, which although dated has continued to rise in popularity and attendence. This course has now been updated and superceeded a brand new one which kicks off this week, and is open for any interested practitioners to join.
Learning how to develop end to end MLOps systems becomes key as the production machine learning requirements and use-cases increase. This is the largest and fastest growing MLOps course in github, and includes an exhaustive list of themes covered in detail with practical examples and tips.
Deep learning has had increasing impact acros various sub-fields of machine learning including recommender systems. This survey provides an excellent overview and deep dive into the broad range of use-cases where deep learning has been adopted throughout the field of recommender systems.
A great summary for a great book on effective software testing, a topic that is of paramount experience not only in the general software space but it is becoming growingly critical in the ML and MLOps spaces. This article covers key learnings and summaries of software testing best practices, as well as references for suggested deeper reads of the book.
Every year the stackoverflow team releases their annual developer survey, this year containing answers from over 70,000 developers, and spanning across the areas of technology, tooling, methodologies and beyond.
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:
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!
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© 2018 The Institute for Ethical AI & Machine Learning