The MachineLearning EngineerIssue #124

This week in Issue #124:

• Automated ML Evaluation at Scale
• Tech Capabilities for MLOps
• Selecting ML Feature Eng Method
• Best Practices in Data Governance
• The NLP Index with 3000+ Repos
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

Forward email, or share the online version on 🐦 Twitter, 💼 Linkedin and 📕 Facebook!

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!

Automated ML Eval at Scale

Machine Learning models come in different sizes, shapes and flavours - each with varying hardware (and software) requirements. This talk provides a deep dive into the motivations and best practices for benchmarking specifically applied to machine learning, as well as the practical steps and frameworks we can leverage to introduce automation to performance evaluation.

Tech Capabilities for MLOps

Data Science Architect Theofilos Papapanagiotou has put together a very comprehensive overview of the MLOps ecosystem, and covers the full spectrum including the workflows, the ML pipeline, and each of the sub-sections such as fefature stores, training pipelines, serving, etc.

Selecting ML Feature Eng Method

A popular post from machine learning mastery covering the different approaches to choose a feature selection method for machine learning. It also introduces some key concepts and tips when performing feature engineering.

Eng Best Practices in Data Gov

The Data Exchange podcast dives into conversation with Immuta Cofounder and CTO Steve Touw to discuss the importance of data governance across organisations. In this podcast they dive into data discovery, privacy, securty and governance.

The NLP Index with 3000+ Repos

A great resource published this week with over 3000 code repositories for practitioners and researchers. In provides features to search across the arxiv index and includes further resources such as link to the paper and github repo.

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!