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Issue #185
This #185 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 #185:
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!
Obtaining practical MLOps knowledge can often be hard for practitioners looking to get started in the field due to the sheer amount of growing tools. This open source course covers a an extensive breadth of content including best practices of the overarching topic, and practical insights on experiment tracking, model management, orchestration, model deployment, monitoring and beyond.
As the MLOps ecosystem continues to grow there is a need for a taxonomy to classify the different tools and frameworks in the field. This IEEE paper encompasses an attempt to provide a taxonomy and methodology to exactly this challenge, providing a summary of key areas and tools in the ecosystem.
The experimentation tracking and experiment management phase of the ML model lifecycle has seen a significant growth in maturity and consolidation in the last decade. This survey provides an interesting exploration on key metrics to compare popular open source and closed source tools used in for experimentation and model management.
Shipping systems to production is an absolutely critical phase in the software development lifecycle, and particularly imporant in specialised field such as MLOps which focuses particularly on the operation of production-grade machine learning systems. This article provides a great intuition and set of best practices / principles involved in productionisation of software, such as testing, automation, bad practice and good practice, between other great insights.
These reverse tech interview tips for interviewees are essential; there are extensive and growing number of resources to prepare for technical interviews, however this great resource provides a fantastic perspective - namely meaningful questions that candidate can ask interviewers, including questions about the tech, the team, coworkers, the company, and more.
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!
© 2018 The Institute for Ethical AI & Machine Learning