The MachineLearning EngineerIssue #152

This week in Issue #152:

• MLOps London November Meetup
• Scalable Explainable NLP Search
• KubeCon North America 2021
• AI begins with Data Quality
• Reflections 10k hrs Programming
• 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!

MLOps LDN November Meetup

The MLOps London meetup comes back with its 2nd online & on-prem session looking to bring together practitioners that work with production machine learning, and will be doing their first kick-off event this coming month.

Scalable Explainable NLP Search

Insightful piece from Standford AI Lab blog covering retrieval-based NLP systems that provide state-of-the-art capabilities for tasks like answering open-domain questions and verifying complex claims.

KubeCon North America 2021

The KubeCon 2021 North America conference videos have now been published. These are a fantastic resource for MLOps practitioners to gain insights around key trends in the Kubernetes world including observability, security, real world applications, best practices and more.

AI begins with Data Quality

The Data Exchange podcast dives into conversation with Anomalo CTO to cover key insigths in the area of data quality, and provide some insights on data engineering, most important chalelnges, end-to-end dataops and more.

Reflections 10k hrs Programming

An interesting and succint overview that aims to extract key lessons from a career of software development, providing 31 different high level ideas that aim to capture best practices and lessons.

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!