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Issue #232
This 232 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 🚀
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 This week in the ML Engineer:
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Training GPT-3 in a state-of-the-art U.S. data center could consume up to 700,000 liters of freshwater 🚿 The article discusses the often overlooked water footprint of AI models and reveals that training large models like GPT-3 or GPT-4 can consume vast amounts of water in data centers, which is concerning due to freshwater scarcity. The authors explain water usage in data centers, highlighting both direct consumption for cooling servers and indirect consumption for electricity generation. They suggest that the 'when' and 'where' of AI model training can greatly influence water usage effectiveness, proposing a methodology for fine-grained water footprint estimation and advocating for transparency in disclosing such information. They emphasize the need for a holistic approach to sustainability, considering both water and carbon footprints.
In this talk, Andrej Karpathy shares insights into the training and application of large language models (LLMs), like GPT-4. The training process involves four major stages: pre-training, supervised fine-tuning, reward modeling, and reinforcement learning. He highlights the importance of tokenization during pre-training and discusses 'constraint prompting' for controlling LLM outputs. He also touches upon the practice of model fine-tuning, suggesting the use of techniques like LoRA, while warning against the complexity of reinforcement learning from human feedback (RLHF). Karpathy recommends using detailed prompts and experimenting with tools to optimize LLM use. Despite their limitations, such as bias and susceptibility to attacks, he views LLMs as valuable co-pilots in low-stake applications, while expressing admiration for the breadth of knowledge encapsulated by models like GPT-4.
The 2023 State of Data + AI report examines trends in data and AI based on data from over 9,000 global Databricks customers. Key findings include a significant increase in the use of Natural Language Processing and Large Language Models, dominance of open-source and data integration tools in data and AI stacks, with Microsoft Power BI leading, and a rising trend of unifying data, analytics, and AI on platforms like Lakehouse.
This article outlines insights and learnings from experimenting with GPT-4 to autonomously create a VSCode extension for adjusting the heading level of selected Markdown text. The code was generated using the smol-ai framework. Despite a simplified prompt, GPT-4 generated the necessary files and satisfactory code, handling commands and edge cases. The author observed that improvements could be achieved by providing more detailed prompts, utilizing up-to-date information, generating tests, and defining a checklist for high-quality VSCode extensions.
In this article, AI pioneer Andrew Ng discusses his shift from "bits to things," focusing on applying AI to the manufacturing industry. He highlights the challenges faced from data-centric AI, emphasizing that data is becoming more important than models. This approach involves empowering domain experts to express their knowledge through data engineering, especially where data is scarce. Andrew also anticipates the rise of foundation models in computer vision, similar to GPT3 in natural language processing. Looking forward, he believes the AI industry should concentrate more on small data and data-centric AI, a process which he admits will require substantial work and innovation.
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.
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