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The MachineLearning EngineerIssue #300

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Issue #30015/09/24llmsai-agentsmlops
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Issue #300 🤖 - Survey: State of Prod ML 2024, OpenAI Reasoning with LLMs, Copilot is making Devs Worse, The ML Engineering Open Book, Rise of OSS Time Series DBs + more 🚀

Today we celebrate together our 300th Issue 🚀🚀🚀 This is a HUGE milestone we want to celebrate with YOU! As part of this we are launching a survey on The State of Production ML, and your contribution would make a significant difference to the whole ML ecosystem ⭐

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This week in Machine Learning:

Survey: State of Prod ML 2024

As we celebrate our 300th newsletter we invite everyone to contribute to an exciting survey on the State of Production Machine Learning! We have designed the questions to provide meaningful insights on the current landscape of production ML in 2024. By participating, you’ll contribute valuable information about the machine learning tools and platforms you use and your ML ecosystem. Your input will help create a comprehensive overview of common practices, tooling preferences, and challenges faced when deploying models to production, ultimately benefiting the entire ML community 🚀

OpenAI Reasoning with LLMs

OpenAI has introduced O1 - a new large language model trained with reinforcement learning to perform complex reasoning using a hidden chain of thought to improve problem-solving abilities. This new release seems to introduce a different approach over previous models like GPT-4o, which is now specialising on benchmarks for mathematics and science expertise, and programming competence. In practice there has been a broad range of mixed responses, but there seems to be a strong claim for this approach to have some strong potential for improving safety and alignment by allowing the model to internally reason about safety rules.

Copilot is making Devs Worse

Copilot is Making Programmers Worse at Programming: AI code-generation tools like GitHub Copilot have potential to improve productivity, however they can also be double-edged sword which may worsen programmers’ fundamental skills by introducing over-reliance on auto-generated code. This reliance can then lead into hindering development on core programming competencies, such as reducing problem-solving abilities, and introducing a lack of ownership over code quality. Someone is going to have to maintain all the code that is written by AI, so we need to be mindful that the age-old “clean-code” principles are not going anywhere even with these intelligent tools rising.

The ML Engineering Open Book

A great resource diving into Machine Learning Engineering, providing resources specialised for large language models (LLMs) and multi-modal models (VLMs): Great repo that provides practical methodologies, tools, and step-by-step instructions covering critical aspects across the production ML lifecycle such as hardware considerations (compute, storage, network), orchestration with SLURM, training and inference strategies, debugging techniques, and performance optimization.

Rise of OSS Time Series DBs

Open source time series databases like Prometheus and InfluxDB have become growingly popular for production machine learning practitioners to efficiently store and query large volumes of time-stamped data needed for monitoring models, detecting anomalies, and forecasting resources: Traditional relational databases may struggle with the scale and performance demands of time series data if not optimized accordingly - this has led to the rise of open source solutions that offer better stability, efficiency, and scalability for these use-cases. This is an interesting deep dive from VictoriaMetrics, which covers some of the shortcomings from existing solutions as well as their approach to addtessing these.

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.

Upcoming conferences where we’re speaking:

Other upcoming MLOps conferences in 2024:

In case you missed our talks:

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!

OSS: Policy & Guidelines

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!

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The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.

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