The MachineLearning EngineerIssue #139

This week in Issue #139:

• StackOverflow 2021 Dev Survey
• Alibi for ML Explainability
• Data Science Role Evolution
• CPU Transformer Optimization
• Open End-to-end MLOps Platform
• 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!

StackOverflow 2021 Dev Survey

The annual StackOverflow survey has been released, collecting key insights from over 80,000 developers, presenting interesting insights around general trednds, including insights specific to the Data Science & ML space.

Alibi for ML Explainability

A recent paper published in JMLR outlining Alibi Explain, an Open Source Python library for explaining predictions of machine learning models, offering a broad range of techniques for various data formats and explainability types.

Data Science Role Evolution

The Data Exchange dives into conversation with Lyft Data Science Manager Sean Taylor, where he shares insights on how the data science role has evolved during the years, including management duties, recruiting, mentoring, tooling and higher level challenges.

CPU Transformer Optimization

A comprehensive overview of optimization techniques for transformer-like models in CPU, particularly relevance for inference performance in models. It covers the motivations, tooling, approaches and hands on steps carried out, together with the reslts achieved.

Open End-to-end MLOps Platform

An interesting blog post showcasing a recent open source project that aims to provide an end to end MLOps platform that puts together best-in-class open source tools to cover the end to end lifecycle of ML models.

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!