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Issue #125
This week in Issue #125:
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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! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
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.
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.
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.
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.
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
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:
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!
© 2018 The Institute for Ethical AI & Machine Learning