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THE ML ENGINEER — WEEKLY NEWSLETTER

The MachineLearning EngineerIssue #243

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Issue #24313/08/23ml-educationexplainabilityllms
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Issue #243 🤖 - Stanford Course on Intro to ML, ML Models Learn vs Generalise, Building Llama from Scratch, MLFlow Tracking and MinIO, MLSecOps Kubeflow Exploration + more 🚀

This 243 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 45,000+ subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀

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This week in the ML Engineer:

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Stanford Course on Intro to ML

Stanford releases an updated version of their classic introduction to machine learning course. This 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.

ML Models Learn vs Generalise

Do Machine Learning models only memorize or do they actually generalize? In 2021, researchers identified a “grokking” phenomenon where certain machine learning models transition from memorizing to generalizing after extended training. Using modular addition and binary sequences as test cases, this study reveal that the occurrence of grokking is contingent on specific hyperparameters like weight decay. While observed in smaller models, there’s evidence to suggest that larger models might also exhibit grokking. The article emphasizes the importance of understanding these dynamics, and explores a step-by-step approach to interpret larger models by starting with simpler ones.

Building Llama from Scratch

Inspired by Karpathy’s Makemore series, this article offers a step-by-step guide on implementing the Llama starting small using the TinyShakespeare dataset. The approach in this tutorial is iterative, starting with basic models and gradually integrating Llama’s unique features like RMSNorm, Rotary embeddings, and the SwiGLU activation function. There are various tips included, such as consistently checking tensor shapes, testing layers across different sizes, and ensuring model components function as intended. This article underscores the importance of simplicity and iterative testing in successfully implementing complex machine learning models.

MLFlow Tracking and MinIO

The MinIO team has put together an overview on how to use MLflow for managing the machine learning lifecycle with a focus on tracking experiments, logging parameters, metrics, and artifacts. Using the MNIST dataset as an example, this tutorial demonstrates how to utilize MLflow’s Tracking API to log experiment details and visualize them via the MLflow UI. This article also shows how to integrate MLflow with MinIO as an object store interface for efficient and reliable storage of large artifacts, such as trained model artifacts, ensuring streamlined tracking and storage in machine learning workflows.

MLSecOps Kubeflow Exploration

An interesting security vulnerability report has been published on the machine learning pipelines framework Kubeflow. This article encompasses a reported vulnerability. These vulnerabilities emphasise the importance of machine learning security and MLSecOps; in this case these can lead to account hijacking, internal network attacks, and unauthorized data access. The research also unveiled simple exploit tool Kubejack.py which demonstrates the potential risks. Users of Kubeflow are urged to be cautious and consider necessary security measures, but for broader machine learning practitioners this serves as a reminder of the importance of security in 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:

Relevant upcoming MLOps conferences:

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!

About us

The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning.

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