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Issue #220
This 220 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 πŸš€
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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 πŸ€– If you havent, you can join for free at ⭐
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
Large Language & Image Models
Productionising Machine Learning Systems at Scale is one of the biggest challenges this year, and Large Language/Image models introduce complex challenges πŸ’‘ Our talk from PyData Global 2022 is now on YouTube, and provides a detailed overview of the challenges and solutions for productionising Large Image/Text/Anything Models. In this resource we take a relatively amusing approach, where we deploy a ML Pipeline with a GPT model as the pre-processor and a text-to-image GenAI model as the post-processor. This allowed for a "creative" workflow where images are created from a single word, into a generated phrase, into an image. The code is fully open source so do test it out or please do contribute with a PR πŸ”¨
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective πŸ€– Cambridge researchers share an insigthful and comprehensive survey on production machine learning systems demystifying the emerging topic of data-centric ML. This research paper focuses particularly on challenges and insights around deployment, monitoring and maintenance of machine learning systems.
Real time high-performance data at Coinbase πŸͺ™ An interesting series from Coinbase discussing their journey tackling high-performance data challenges across their organisations at scale. In this resource they provide useful information on their architecture, technologies, benchmarks, principles and next steps.
NVIDIA OSS Recommender System meets the MLOps ecosystem: building a production-ready RecSys pipeline on cloud πŸ”This post provides a high level overview of production challenges when adopting and productionising recommender systems. It provides a practical example to tackle a real-life challenge, providing an intuition on the architecture as well as code for training, testing, serving and beyond using ecosystem tooling such as Metaflow, DBT, and more.
20 Lessons from 20 years in developing software πŸ–₯️ A great high level article providing 20 points of advise from a long career in software engineer, aiming to outline a set of principles that are important to consider for a meaningful and consciencious approach to software as a applicable and useful craft.
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!
Β© 2018 The Institute for Ethical AI & Machine Learning