Issue #262 - The ML Engineer 🤖
The Machine Learning Engineer 🤖 #262 - Machine Learning at Stanford, Don't Build a Vector Database, LLM 3D, Scaling Pinterest to Millions, Optimising LLMs + more 🚀
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This week in Machine Learning:
Machine Learning at Stanford
Don't Build a Vector Database
LLM 3D Visualisation Demo
Scaling Pinterest to Millions Users
Optimising LLMs from Datasets
Upcoming MLOps Events
Open Source ML Frameworks
Awesome AI Guidelines to check out this week
+ more 🚀
Stanford releases an updated version of their classic introduction to machine learning course: This course is available for free online and offers a comprehensive exploration of machine learning, starting with foundational concepts like supervised learning and linear algebra, and advancing to complex topics such as deep learning, neural networks, and reinforcement learning. The sessions dive into the practical tools such as Python/Numpy covering evaluation metrics, bias-variance trade-offs, etc. Special highlights include a guest lecture on the societal impact of ML and insights into decision trees, boosting, and model-based RL.
Thinking of Vector DBs as a search engine for LLMs, not as memory expanders: Great article which dives into considerations around LLM application concepts, and emphasizes the importance of building robust search engines, blending traditional methods with embeddings, and leveraging language models for query structuring and re-ranking to improve search quality. This article highlights the reduced effort required to build advanced search systems with recent AI advancements, but also notes the ongoing challenge of evaluating and monitoring these systems to ensure their effectiveness.
An outstanding interactive 3D visualisation of LLM architectures: This interactive webapp offers a detailed walkthrough of different (simplified) LLM architectures within the GPT large language model family. It provides a visual overview of the inference process rather than training, and demonstrates how the model sorts sequences of tokens using embeddings and transformer layers, with a specific example of alphabetically sorting "C B A B B C" into "ABBBCC". The guide was inspired by Andrej Karpathy's minGPT project and provides a visual learning resource to build an intuition on the internals of LLMs.
Scaling Pinterest to Millions Users
How Pinterest scaled to 11+ million users with only 6 engineers: A great summary of Pinterest's talk at Qcon 2012 on how they scaled 11.7 million monthly active users with just 6 engineers, highlighting key lessons for software practitioners. This journey emphasises the use of proven technologies, where Pinterest transitioned from a complex mix of newer technologies to a more streamlined architecture centered around MySQL and Memcached. Key strategies included simplicity in design, manual database sharding over clustering to ensure better load balancing and high availability, and a focus on efficient data structuring. This approach underscores the importance of proven, simple solutions in rapidly scaling tech platforms that are built through minimal engineering resources.
Sebastian Raschka on Optimizing Large Language Models (LLMs) from a Dataset Perspective: Great article which discusses enhancing LLM performance through instruction finetuning using curated datasets. Key strategies include using both human-created and LLM-generated datasets with an emphasis on quality over quantity, as exemplified by the LIMA dataset. The article provides practical guidance on dataset preparation and finetuning techniques, particularly relevant for the NeurIPS LLM Efficiency Challenge. It highlights the importance of dataset curation in improving LLMs for specific applications and offers insights into research directions like dataset merging and ordering, aiming to aid production ML practitioners in optimizing LLMs effectively.
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.