The MachineLearning EngineerIssue #157

This week in Issue #157:

• Call to Build Models like OSS
• Cross-Vendor GPGPU at CppCon
• The 18 Highest Paying Dev Roles
• Making Language Models Smart
• Scaling Teams Parallel Systems
• 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!

Call to Build Models like OSS

An interesting article posing a call to action to encourage similar principles and philosophies present in open source software development in the AI model training world, namely outlining the benefits on communication, cooperation, ineroperability, contributions, backwards compatibility and more.

Cross-Vendor GPGPU at CppCon

Our CppCon 2021 talk is now out 🚀 In this session we provide a hands on introduction to cross-vendor GPU acceleration for general compute using C++ as well as machine learning use-cases using the Vulkan & Kompute open source projects.

The 18 Highest Paying Dev Roles

An overview of some key software development roles and specialisations that have currently grown to become some of the highest paid roles, ranging across architecture, cloud, web, mobile and data.

Making Language Models Smart

The data exchange podcast comes back this week in conversation with AI21 Labs Co-founder and Stanford Professor Yoav Shoham where they dive into the power of NLP and language models as well as key opportunities and the future in the field.

Scaling Teams Parallel Systems

This article provides an interesting set of concepts analogous to the philosophy behind the “mythical man-month” through parallel computing systems concepts, showing the relationship between increased stakeholders and output.

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:

• 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!