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Issue #201
We are still buzzing from hitting our 200th milestone last week, so we want to continue celebrating this week on all the milestones achieved so far 🚀

We want to thank YOU for supporting our work, we are looking forward to continue driving forward & contributing to the conversations of ML engineering 🚀 Bring on 201 more!
We will be at KubeCon North America this week where we'll be doing an opening keynote at the K8s AI day, as well as a talk on E2e Metadata Interoperability, come join us and celebrate with us!
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This week in the MLE #201:
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!
Kubernetes is becoming a common substrate for AI that allows for workloads to be run either in the cloud or in its own data center, and to easily scale. Join this next week at the KubeCon North America 2022 where we'll be doing the opening keynote at the Kubernetes AI Day, followed by a fantastic set of sessions on cloud native AI.
Many organizations have invested in a central data lake, ofter realising the solution becomes the bottleneck. The concept of the data mesh has grown in popularity - this article provides an architectural and practical definition of this paradigm. It covers principles, concepts and practical examples.
Collecting data for machine learning and analytics projects can be time consuming as well as costly. This paper provides an interesting approach to data collection, where a process is introduced to optimize the amount of data collected through an iterative cycle that can help lead to comparable results with less resources.
Metadata will be the foundation for data governance solutions, data catalogs, and other enterprise data systems. This article provides a great overview of the current state of the metadata management landscape, as well as the tools in the ecosystem.
As organisations adopt data-driven decision making through robust and scalable infrastructure, there is a realisation of the importance for moving from a project mentality into a product/platorm mentality. This has come with the rise of the data product manager role - this article provides a great overview of what this role consists of, and how it's been evolving based on the still-evolving data ecosystem.
Upcoming MLOps Events
The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.
Conferences we'll be speaking at:
Other relevant upcoming MLOps conferences:
Open Source MLOps Tools
Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. 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. Four featured libraries in the GPU acceleration space are outlined below.
  • 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 open source and open community events that are not listed do give us a heads up so we can add them!
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:
  • MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
  • 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.
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