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Issue #182
This #182 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 #182:
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!
Optimization of machine learning models can bring benefits beyond speed improvements, including reduced hardware requirements, smaller and more secure artifacts, and overall cost reductions. In this talk we cover practical steps and tools that can be leveraged to perform optimizations on machine learning models through scalable patterns, leveraging tools like Huggingface, ONNX, MLServer and Seldon Core.
Flyte is a popular open source platform that enables complex mission-critical workflow automation for machine learning processes at scale. The data exchange podcast dives into conversation with the CTO of "Union", the open core company behind Flyte. In this conversation they dive into motivations for robust ML orchestration platforms, challenges and plans to grow the project & community.
Data-centric machine learning has become a growingly important topic of theoretical and applied research in the MLOps space, and implementations using streaming platforms such as Kafka have been leading the charge. This talk provides an insightful set of machine learning patterns inferred from large scale use of data streaming pipelines in ML at scale.
The Go programming language has been raising in popularity at breakneck speed since its inception. This insightful ACM Communications article explores the attributes and principles that contributed to the robust and widely-loved features of the Go programming language.
The topic of developer productivity is a growing field being explored, with often interesting and insightful perspectives. The Microsoft research team presents and debunks some of the common "Myths" in developer productivity, and provide a well-thought "SPACE" framework that presents the importance of concepts such as dev satisfaction and developer tooling as key considerations.
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