The MachineLearning EngineerIssue #125

This week in Issue #125:

• AI Adoption in Enterprise 2021
• State of Data Engineering in 2021
• Choosing Best E2E MLOps Tools
• Python Beginner to Advanced Resources
• Importance of Reliable Metadata
• 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!

AI Adoption in Enterprise 2021

O’Reilly VP Mike Loukides shares a snapshot of the current state of AI adoption in industry. In this report they cover some key highlights including the challenges on skills, data quality, maturity of systems and sub-sector specific insight - between others.

State of Data Engineering in 2021

The LakeFS team presents a comprehensive overview of the different areas relevant to data engineering, as well as key trends and tools. In this post they cover data ingestion, storage, processing and metadata management.

Choosing Best E2E MLOps Tools

A high level overview of the MLOps ecosystem and the approaches that can be taken to select a best-of-class integration that fits organisational requirements, as well as the requirements that have to be taken into consideration when stitching together solutions from multiple suppliers.

Python Beg. to Adv. Resources

As practitioners there is a great incentive to continuously improve our skills and knowledge, and this article provides resources for beginner, intermediate and advanced Python developers, and spans across a broad range of tutorials, blogs, courses and books.

Importance of Reliable Metadata

As the requirements for DataOps and MLOps increases, there is also an increasing importance in the systems to manage the metadata of these resources. This post provides insights on the motivations, concepts, challenges, and solutions around metadata systems.

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!