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8
Issue #219
THE ML ENGINEER 🤖
 
This 219 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 25,000+  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 the ML Engineer:
 
 
Thank you for being part of over 25,000 ML professionals and enthusiasts who receive weekly articles & tutorials on production ML & MLOps 🤖 If you havent, you can join for free at https://ethical.institute/mle.html
 
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just 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!
 
 
 
The NeurIPS 2022 Workshop on Challenges in ML Systems has now released the recordings, including our keynote on ML Security 🚀 This was a fantastic resource diving into real world use-cases and best practices Deploying and Monitoring machine learning systems, and has a fantastic set of speakers across industry and academy covering topics in ML Security, federated learning, monitoring and more. You can find our keynote on Production ML Security directly together with the respective slides and resources.
 
 
MIT launches a course on Data-Centric AI 🚀 This is the first-ever course on Data-Centric AI. This class covers algorithms to find and fix common issues in ML data and to construct better datasets, concentrating on data used in supervised learning tasks like classification. All material taught in this course is highly practical, focused on impactful aspects of real-world ML applications, rather than mathematical details of how particular models work. You can take this course to learn practical techniques not covered in most ML classes, which will help mitigate the “garbage in, garbage out” problem that plagues many real-world ML applications. Videos are available on Youtube, together with the respective course materials.
 
 
The 2023 MAD (Machine Learning, Artificial Intelligence & Data) Landscape has now been released 😈 The MAD landscape is now an annualy-released resource covering the AI/ML landscape, together with market trends, trends in data infrastructure and trends in general ML & AI.
 
 
Applied ML Repo (23k+ ⭐) A fantastic resource containing curated papers, articles and blogs on data science and machine learning in production. For anyone figuring out how to implement ML in your projects, this resource provides how organisations did it: 1) How the problem is framed, 2) what machine learning techniques worked, 3) why it works, 4) what real-world results were achieved.
 
 
Unleashing ML Innovation at Spotify with Ray 💡 Spotify founded its machine learning (ML) platform in 2018 to provide a gold standard for reliable and responsible production ML. In early 2020, their ML Platform expanded to cover the ML production workflow for Spotify’s ML practitioners with four core product offerings. This article dives into the next evolution of Spotify’s ML infrastructure.
 
 
 
 
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 spoke at recently with published video:
 
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:
 
  • MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
  • AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems.
  • An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles.
  • ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018.
 
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