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Issue #224
This 224 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 30,000+  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 30,000 ML professionals and enthusiasts who receive weekly articles & tutorials on production ML & MLOps πŸ€– If you havent, you can join for free at ⭐
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Facebook's MLOps Ecosystem
Facebook/Meta shares their approach to organisation-wide MLOps πŸ’‘ Facebook/Meta has developed measurement processes to manage AI models effectively and efficiently, and shares techniques that can be applied broadly in other organizations. They discuss the goals and principles of AI model management, Meta's ML-Ops ecosystem, and the importance of consistently defining key concepts in AI model management. The taeam also emphasize the need for a clear metadata architecture to bridge specific system implementations via common labels. If you are interested in the topic you can check out the recording of our talk on Metadata Systems for End-to-End Data & Machine Learning at PyData Global 2022.
Twitter Open Sources its tweet-recommendation algorithm ⚑ This is a fascinating resource, as it showcases the recommendation algorithm internals, which uses a set of core models and features to extract latent information from tweet, user, and engagement data. The algorithm is composed of candidate sourcing, ranking, and filtering stages. The ranking stage uses a neural network trained on tweet interactions to optimize for positive engagement, and heuristics and filters are applied to create a balanced and diverse feed. We have also seen already some controversial code being removed followed by a swarm of comments / gifs / memes in the commit hash.
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 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”¨
GPT-4-ALL making the ChatGPT experience accessible to all πŸ€– Following the fascinating developments from last week which saw LLaMa 30b running on 6GB RAM via mmap fundamentals, we now see projects like GPT-4-ALL providing tooling to leverage these innovations at the application level in ever-simpler workflows. These projects are fully open source and benefit from community interactions and feedback so do feel free to contribute.
Bloomberg has released a new language model called BloombergGPT, specifically trained on financial data πŸ’Έ With 50 billion parameters and 363 billion tokens it claims itself as the largest domain-specific dataset. The model has outperformed existing models on financial tasks without sacrificing performance on general LLM benchmarks, such as those of GPT-NeoX and OPT 66B. BloombergGPT can perform financial question answering, sentiment analysis, NER, and generate valid Bloomberg Query Language and short headline suggestions.
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.
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