The MachineLearning EngineerIssue #134

This week in Issue #134:

• From ML Model to Microservice
• HuggingFace Founder on MLOps
• How to NOT Measure Latency
• MIT Deep Learning Intro Course
• Data Mesh Design & Architecture
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone’s support!

From ML Model to Microservice

An exciting collaboration blog post between Seldon and Microsoft that outlines best practices and practical steps to deploy a GPT-2 Model with Triton ONNX Runtime using Seldon Core in Azure Kubernetes.

HuggingFace Founder on MLOps

HuggingFace CEO joins the MLOps podcast to dive into conversation around the challenges and best practices of deploying large language / transformer-based models in production.

How to NOT Measure Latency

A classic in the performance space which outlines some of the biggest pitfalls of performance evaluation - although this talk was presented a while ago, it is as relevant today in the MLOps space as it is in the general microservice performance evaluation context.

MIT Deep Learning Intro Course

A fantastic and comprehensive online course from MIT that covers an introduction to the different areas of deep learning, and provides quite a breadth on all the sub-topics it spans across.

Data Mesh Design & Architecture

An extensive analysis on the core principles and logical architecture of data meshes. It covers the premise and motivations, and proposes concepts such as data as product, self serve data platform, federated computational governance, and the principles that surround these.

OSS: GPU Accel. Frameworks

The topic for this week’s featured production machine learning libraries is GPU Acceleration Frameworks. 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. The four featured libraries this week are:

• Vulkan 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 libraries that are not in the “Awesome MLOps” list, please do give us a heads up or feel free to add a pull request!

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:

• 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
• From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices

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!