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This week we will be speaking at the PyData Berlin 2023 πŸš€ Come join our session on The State of Prod ML in 2023 πŸ₯³πŸΎπŸŽˆ
Issue #226
This 226 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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The Actually Open AI Chat-GPT is now officially released πŸš€ Open Assistant is an open-source project that aims to provide a chat-based large language model to everyone - the project is also enabling data collection to submit, rank, and label model prompts and responses to train models via crowd-sourcing and enable an open accessible AI assistant framework that can perform meaningful tasks, research information, and be personalized and extended by anyone.
Large Language Models are driving exciting and high-potential use-cases through agent-chain-tool architecturesπŸ’‘ This is a fantastic tutorial by LangChain creator which dives into the concepts that are powering the ever-more-mind-blowing use-cases with Large Language Models. This dives into practical examples showing how to supercharge LLMs with agents, tools and chains, as well as enhancing through external sources to unlock advanced capabilities.
One of the best resources for Recommender Systems architectures πŸ€– The evolution of Recommender Systems through architectural Blueprints. This resource does a great job of compiling several of the most comprehensive end-to-end recommender system architectural blueprints, providing a high level overview of each as well as interesting thoughts. Finally it proposes its own take with a blueprint that encompasses all the relevant components through a data-centric perspective.
Google shares their agenda for Responsible AI progress πŸ’‘ They emphasise the importance of a collective effort from citizens, educators, academics, civil society, and governments to shape the development and use of AI. They also cover the principles proposed for the development of responsible AI policies and frameworks, such as building on existing regulations and promoting transparency that facilitates accountability.
ChatGPT Productivity Hacks
ChatGPT hacks to increase developer productivity πŸ€” This video dives into some of the recent growingly popular productivity hacks that developers have started adopting to increase their productivity. These include describing code, supporting on debugging, translating across programming languages, requesting snippets, writing unit tests, modifying existing code and writing documentation.
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