The MachineLearning EngineerIssue #252

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The ML Engineer this week:

• Multimodality and LLMs Overview
• State of AI Report for 2023
• Stable Difussion from Scratch
• Inverted Transformer Forecasting
• Introduction to Modern Statistics
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

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If you are a Machine Learning Practitioner looking for an interesting opportunity, I’m currently hiring for a few roles including Applied Science Manager, Applied Scientist, Analytics Team Lead, and Customer Analyst - do check it out and do feel free to share broadly!

Multimodality and LLMs Overview

Chip Huyen has put together a comprehensive overview of Multimodality and Large Multimodal Models. The article delves into the rise of LLMs that operate beyond single data modalities, marking a shift from traditional ML models limited to text, image, or even audio. This article covers quite an extensive case for the potential of multimodality in LLMs following from DeepMind’s recent GPT4V release. The article dives into: 1) exploring multimodality’s context, 2) discussing fundamental multimodal systems like CLIP and Flamingo, and 3) highlighting ongoing research areas in LMMs such as efficient training techniques + new systems like BLIP-2 and LLaVA.

State of AI Report for 2023

The State of AI Report for 2023 is out: This edition discusses key trends like GPT-4’s surprise to the world, the efforts to mimic proprietary model performance, and real-world breakthroughs driven by Language Models and diffusion models in life sciences. The report also highlights the booming compute industry led by NVIDIA, the rise in generative AI startups amidst a tech valuation slump, and the ongoing global safety debate concerning AI, emphasizing the necessity for robust evaluations of state-of-the-art models.

Stable Difussion from Scratch

A great resource to build a strong intuition on the fundamentals of Stable Difussion by building it from scratch. This video covers a deep dive starting with foundational concepts, including the intricacies of generative models, the mathematics behind them, and their applications, including text-to-image, image-to-image, and inpainting processes. You can also access the repository and the PDF slides.

Inverted Transformer Forecasting

Transformer-based forecasting continues to see insightful developments, this time from Tsinghua University researchers on an inverted transformer architecture. This is an interesting development similar to the TSMixer architecture that Google released a few months ago. This “iTransformer” architecture leverages an inverted which instead of treating each time step as a token, it embeds the entire variable history as individual tokens, addressing the typical Transformer limitations in this domain. It is suggested that this better captures multivariate correlations and encodes temporal series representations.

Introduction to Modern Statistics

A fantastic book providing an in-depth introduction to modern statistics with great accompanying resources, and for free (with a pay-what-you-wish option)! This v2 resource is in progress but with the first edition available online covering a broad range of topics including data introduction, exploratory data analysis, regression modeling, foundations of inference, statistical inference, and inferential modeling. The textbook comes with supplementary materials like slides, labs, and interactive tutorials, which would be useful for practitioners aiming to enhance their understanding of modern statistics in the context of production machine learning.

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.

Check out our “MLOps Curriculum” from previous conferences:

• 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

Relevant upcoming MLOps conferences:

• MLSys - 4th June @ Florida
• Women+ in Data AND AI - 30th June @ Berlin
• EuroPython - 17th July @ Prague
• EuroSciPy - 14th Aug @ Basel
• MLOps World - 25th October @ Austin
• ODSC West - 31st October @ California
• KubeCon NA (AI Track) - 6th November @ Chicago
• The AI Summit - 6th December @ New York

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: Awesome AI 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!