The MachineLearning EngineerIssue #264

🥳🥳🥳 Happy New Year 2024!! 🥳🥳🥳

This week we CONTINUE to celebrate 5 years since starting this weekly Machine Learning newsletter 🎉 What started with just one commit, today now has almost 60,000 subscribers 🚀 And not a single Sunday missed 🤯 Thank you to everyone for your continued support - in today’s newsletter we share a special edition celebrating our achievements throughout 2023!

Thank you for being part of over 50,000+ ML professionals and enthusiasts who receive weekly articles & tutorials on Machine Learning & MLOps 🤖 You can join the newsletter for free at https://ethical.institute/mle.html ⭐

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This week in Machine Learning:

• 🥳 Celebrating 5 years towards 2024 🥳
• Databases 2023 Year in Review
• Meta’s Audio2Animation AI Model
• Efficient Multimodal OSS LLMs
• Deep Learning on Relational DBs
• Upcoming MLOps Events
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

MLL Celebrating 5 years towards 2024

This week we continue to celebrate the achievements at The Institute for Ethical AI throughout 2023 as we celebrate 5 years since we started this newsletter! During 2023 we reached various large milestones, including breaking through 50,000 subscribers - almost 60k now! Our Product ML Repo reached 15,000 stars and over 2,000 forks! Our GPU Acceleration Kompute project reached over 1,300 stars and broke through 100 forks! This year we also had hugely successful contributions to policy through the ACM’s European Policy Committee, with 3 out of our 4 recommendations being adopted by the UK government on the AI Regulatory proposal, co-authoring the Principles for Generative AI, as well as contributions to the EU’s AI Act, Data Act and Digital Services Act!

Databases 2023 Year in Review

Databases in 2023: A Year in Review (with AI at the helm!) 💡 In 2023, the database industry was marked by the rise of vector databases, fueled by the growing interest in Large Language Models like ChatGPT, which revolutionized semantic search in unstructured data. This trend led to rapid adoption by various DBMS vendors and significant venture capital investment. Concurrently, SQL continued to evolve with the SQL:2023 specification, enhancing graph-structured queries and array data handling. The industry also faced challenges, exemplified by MariaDB Corporation’s struggles and the FAA’s NOTAM system outage, underscoring the risks of legacy systems. Oracle (under Larry Ellison) achieved significant milestones, reflecting the dynamic and evolving nature of the database sector, increasingly influenced by AI and ML technologies.

Meta’s Audio2Animation AI Model

Meta’s new Audio-to-3D-Animation AI model with open source code, pretrained models and datasets: An insightufl new research project from Meta Research which presents a framework for generating photorealistic, full-bodied human avatars from audio inputs, focusing on conversational gestures. They have also made available an OSS PyTorch implementation, including training and testing code, pretrained models, and a specialized dataset. The paper highlights the use of vector quantization and diffusion processes to create dynamic and expressive avatar motions, outperforming existing methods. This work is significant for applications in general computer animated interactions, which for meta seems to continue on their Metaverse quest.

Efficient Multimodal OSS LLMs

TinyGPT-V brings us an exciting step forward towards high-performance LLMs that can run in modest hardware: TinyGPT-V is an innovative multimodal large language model designed for efficient performance on commodity hardware, requiring only a 24G GPU for training and an 8G GPU or CPU for inference. Both the code and the model have now been open sourced, which were built upon the Phi-2 architecture and integrate pre-trained vision modules, boasting 2.8 billion parameters. It stands out for its unique quantization process, making it suitable for various devices including devices with limited memory. TinyGPT-V represents a breakthrough in making advanced MLLMs accessible for a broader range of applications, significantly reducing the computational resources required for high-level multimodal learning tasks.

Deep Learning on Relational DBs

Introducing Deep Learning for Relational Database Data Structures and Graph Relationships: Relational Deep Learning is a groundbreaking approach introduced for efficiently utilizing data from relational databases in machine learning. This method treats databases as heterogeneous graphs, where rows are nodes and primary-foreign key relations are edges, enabling the use of Message Passing Graph Neural Networks for direct learning from multi-table data without manual feature engineering. RDL enhances model accuracy and reduces data preparation time, representing a significant advancement for practitioners dealing with complex, relational datasets. The paper also introduces RelBench, a set of diverse benchmark datasets and an RDL implementation framework, marking a new direction in graph machine learning research applicable to a wide array of AI use cases.

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 MLOps conferences:

• 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 key 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.

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