Issue #311 - The ML Engineer 🤖
Number of ML Models in Prod, Building a GenAI Platform, Open Source Agentic Workflows, 1000+ Python Videos, Advent of Code + more 🚀
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This week in Machine Learning:
Number of ML Models in Prod
Building a GenAI Platform
Open Source Agentic Workflows
Over 1000 Python Videos
Advent of Code 2024
Open Source ML Frameworks
Awesome AI Guidelines to check out this week
+ more 🚀
Almost 70% of teams reported to have less than 100 models in production; however most of them are looking to double the number of models in production within the next 12 months. This provides an interesting snapshot of the production ML ecosystem, as although many teams are operating at smaller relative sclaes, the rate of growth is only increasing; we can see this with 9-15% of teams operating already over 1000 models in production. We are uncovering important insights as part of our survey on The State of Production ML in 2024; please contribute to this valuable investigation on machine learning tools and platforms used in your production ML development. 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 🚀
Chip Huyen has put together a fantastic resource on production architectural patterns and best practices for building GenAI platforms: This is a great guide for production machine learning practitioners on building a robust generative AI platform, starting from a basic model deployment and progressively adding essential components such as enhanced context with retrieval-augmented generation (RAG), safety guardrails to prevent data leakage and manage outputs, model routing and gateways for scalability and control, caching strategies for latency and cost optimization, and complex logic with write actions for advanced capabilities. This is a very much needed resource, as it emphasises the importance of observability and orchestration to monitor, debug, and manage complex AI pipelines effectively.
Our Production ML Github List has reached over 17,000+ stars 🚀 this provides a snapshot of open source tools in the ecosystem and we just added "Agentic Frameworks" to the list! We have put together this list to help machine learning practitioners to deploy, monitor, version, scale, and secure their production ML systems. The new "Agentic Workflow" section captures the tools available for building AI agents and multi-agent systems, including libraries like AgentScope, AutoGen, Chidori, LangGraph, between many others. If you know of any open source framework that is not listed please do give us a heads up or feel free to open up a PR!
This is an absolutely fantastic to browse over 1000 python videos; more importantly for machine learning practitioners you can find a curated collection of machine learning talks from various conferences like PyCon, PyData, and EuroPython: This is quite a great resource for people that are looking to develop continuously, particularly in the ML space, there are a broad range of talks which cover topics such as scalable machine learning pipelines, MLOps best practices, model deployment strategies, deep learning advancements, and practical applications in industries like finance, healthcare, and technology.
The time has arrived to brush up our skills and jump into the advent of code. There will be one programming challenge released every day to take your skills to the test and have some fun, this is a great time to also pick up a new programming language if you've been wanting to explore one for a while. It's also interesting that different to previous years, this time there is a big disclaimer discouraging the use of LLMs for submissions due to the increasing use of these; it is interesting to see how these become more pervasive, and perhaps also how some of these type of challenges will also have to adapt through time to make it such that they can still be providing a challenge despite AI-supported development.
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:
TUM AI@WORK 10th October @ Germany
Other upcoming MLOps conferences in 2024:
ODSC West - 29th October @ USA
Data & AI Summit - 10th June @ USA
AI Summit London - 12th June @ UK
World Summit AI - 9th October @ Neatherlands
MLOps World - 8th November @ USA
In case you missed our talks:
The State of AI in 2024 - WeAreDevelopers 2024
Responsible AI Workshop Keynote - NeurIPS 2021
Practical Guide to ML Explainability - PyCon London
ML Monitoring: Outliers, Drift, XAI - PyCon Keynote
Metadata for E2E MLOps - Kubecon NA 2022
ML Performance Evaluation at Scale - KubeCon Eur 2021
Industry Strength LLMs - PyData Global 2022
ML Security Workshop Keynote - NeurIPS 2022
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!
About us
The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.