The MachineLearning EngineerIssue #126

This week in Issue #126:

• Kubernetes AI & ML Day Talks
• ML Cards for MLOps Governance
• MLOps & Differences to DevOps
• AI Usecases Beyond Automation
• Andrew Ng Course on MLOps
• 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!

Kubernetes AI & ML Day Talks

The Kubernetes AI day took place at this year’s KubeCon Conference, and all the videos are now available for free streaming. This great resource includes content that ranges across productionisation of ML in the context of experimentation, deployment, inference, monitoring and beyond.

ML Cards for MLOps Governance

An interesting initiative that extends Google’s research paper on ML Cards defining metadata of models, into the MLOps space. This blog post proposes a schema for production models that outlines attributes of deployed models that could be standardised for better interoperability across broader systems.

MLOps & Differences to DevOps

This blog post provides a practitioner perspective into MLOps, namely providing an intuition on the nuanced differences between traditional DevOps and MLOps. It dives into the end to end stages of the ML lifecycle including training, deployment, monitoring and further resources.

AI Usecases Beyond Automation

The Data Exchange podcast delves into conversation with DXC Technology Head of AI Jerry Overton, where they discuss centres of excellence for AI, automation in the workforce, human-centered AI and responsible ML.

Andrew Ng Courses on MLOps

Andrew Ng has released a new fantastic resource that aims to demystify the field of MLOps with a theoretical and practical coursera specialisation on the field, where they cover the different areas of MLOps across four courses.

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!