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Issue #183
THE ML ENGINEER 🤖
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If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
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This week in Issue #183:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
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Building a deep learning model is a complex task, full of interacting design decisions, data engineering, parameter tweaking, and experimentation. Having access to powerful tools for versioning, storing, and analyzing every step of the process (MLOps) is essential. This is a fantastic course that dives into end-to-end MLOps with practical and indepth lessons on various key topics.
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The explosion of large models continues in the machine learning ecosystem. Several developments this month are especially noteworthy. The O'Reilly team has put together a great compendium of key trends and resources for June 2022, covering machine learning, general programming, security, hardware and more.
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Search in social networks such as Facebook poses different challenges than in classical web search: besides the query text, it is important to take into account the searcher's context to provide relevant results. The Facebook Engineering & AI team have an interesting paper that covers the architecture and lessons learned introducing embedding-based search retrieval at Facebook.
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The mass adoption of microservices has forced more engineers to understand the implications of that decision within their systems. This is particularly important for practitioners entering and growing in the MLOps space. This article provides a great overview of the top fallacies generally ignored or downplayed by programmers new to distributed applications.
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To combat the prevalence of malware in the open source ecosystem, GitHub now publishes malware occurrences in the GitHub Advisory Database. These advisories power Dependabot alerts and remain forever free and usable by the community. This is particularly important in the MLOps space, as we have emphasised as part of the rise of MLSecOps.
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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:
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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!
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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:
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© 2018 The Institute for Ethical AI & Machine Learning
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The Institute for Ethical AI & Machine Learning
