Issue #274 - The ML Engineer 🤖
The Machine Learning Engineer 🤖 #274 - META's GenAI Infrastructure, MIT Course on Foundation Model, Amazon's FC Foundation Model, ML Engineering, Local AI Images, + more 🚀
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This week in Machine Learning:
META's GenAI Infrastructure
MIT Course on Foundation Model
Amazon's FC Foundation Model
High Quality Local AI Images
Upcoming MLOps Events
Open Source ML Frameworks
Awesome AI Guidelines to check out this week
+ more 🚀
Meta processes hundreds of trillions of AI model data inferences per day - they have announced how two 24,576-GPU clusters are taking their infra to the next level: Meta is advancing its AI infrastructure built on Grand Teton, OpenRack, and PyTorch to support the development of advanced AI models like Llama 3. Meta's AI infra enhancements cover novel network solutions, optimized storage with a focus on efficiency and scale, and significant performance optimizations for handling large-scale AI workloads. Meta's roadmap includes expanding its AI infrastructure to feature 350,000 NVIDIA H100 GPUs by the end of 2024, making it clear that the GPU-wars are at full force across all tech giants.
MIT Course on Foundation Model
MIT Introduces a brand-new on Foundation Models & Generative AI in 2024: This is quite a comprehensive introduction and deep dive into the cutting edge ecosystem of foundation models and generative AI. In this course they cover a broad range of topics, including ChatGPT, Stable-Diffusion & Dall-E, Neural Networks, Supervised Learning, Representation & Unsupervised Learning, Reinforcement Learning, Generative AI, Self-Supervised Learning, Foundation Models, GANs (adversarial), Contrastive Learning, Auto-encoders, Denoising & Diffusion.
Amazon enters the race with their new large-scale zero-shot foundation model for time-series forecasting: Amazon introduces Chronos, a transformer-based architecture model often used for language models, leveraged for probabilistic time series modeling. One of the key innovations introduced is how they approach tokenization of time series data into a fixed vocabulary, which is done through scaling and quantization. Chronos leverages pretrained models, claiming to outperform traditional and deep learning methods in both known and unseen datasets. The paper also highlights future directions including fine-tuning for improved performance and extending the model's application beyond univariate forecasting, suggesting a promising avenue for leveraging developments in language modeling to address complex time series forecasting challenges. It will be interesting to see how the battle of the titans evolves with other giants such as Google and Salesforce launching their models (and how they hold their ground compared to traditional or even common baselines).
Going Beyond Jupyter - a curated set of educational resources for best practices on engineering and system design in machine learning contexts: "Beyond Jupyter" is an excellent educational resource for production machine learning practitioners, which puts together best practices for software design principles within the ML domain. This resource covers practical examples with a case study on refactoring a Jupyter notebook-based project for better maintainability and efficiency, and a review of common anti-patterns.
High quality image generation becomes more attainable for daily use in your personal computer: This resource provides a simple and practical method to reduce the VRAM requirement of the PixArt-α diffusion model to under 8GB, making it accessible on mid-range GPUs like the NVIDIA RTX 2070 or M-series Macbooks. The optimization involves a different approach to loading model components, as well as a custom inference process, which allows the generation of high-quality 1024x1024 images in just 20 seconds.
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:
AI in Production 2024 @ 15th February @ Virtual
WeAreDevelopers 2024 @ 17th July @ Germany
Data Intelligence Day 2024 @ 9th April @ Germany
Other upcoming MLOps conferences in 2024:
FOSDEM (AI & HPC) - 3rd February @ Belgium
State of the Open (Data) - 6th February @ UK
World AI Cannes - 8th February @ France
NVIDIA GTC - 17th March @ USA
KubeCon Europe (AI) - 18th March @ France
MLConf - 28th March @ USA
PyData & PyCon DE - 17th April @ Germany
MLSys - 13th May @ USA
ODSC West - 29th October @ USA
Data & AI Summit - 10th June @ USA
AI Summit London - 12th June @ UK
World Summit AI - 9th October @ Neatherlands
In case you missed our talks:
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.