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Issue #216
This 216 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 20,000+  subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀
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This week in the ML Engineer:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
Github CEO Thomas Dohmke shares thoughts on the AI act supporting Open Source developers 💻 During this open letter he also maks a call-to-action to Europe to drive forward this chance to be a leader in the age of AI. For anyone interested in this area, you can also check out the contributions we made during the open European Commission consultation.
Google releases a Generative AI Text-to-Video-Editing Project 🤯 This latest release showcases impressive realistic performance of video editing, as well as custom-video-creation from a stale image. In the demos they show how an input video can be modified with a text input, as well as how it also works with stale images which are converted into a custom-modified video with relatively high fidelity. Demos: Paper:
The team behind the highly popular Data Version Control (DVC) project have released a highly comprehensive online course 🤖 This online course covers Best practices for going from Jupyter Notebook to Production for Data Scientists and Analysts. This resource covers tools for efficient collaboration, pipelines, model & data versioning, metrics, experiment management and more.
Andrew Ng on Data Centric AI ☯️ Data-centric AI is a growing movement which shifts the engineering focus in AI systems from the model to the data. However, Data-centric AI faces many open challenges, including measuring data quality, data iteration and engineering data as part of the ML project workflow, data management tools, crowdsourcing, data augmentation & data synthesis as well as responsible AI. This talk names the key pillars of Data-centric AI, identifies the trends in Data-centric AI movement, and sets a vision for taking ideas applied intuitively by a handful of experts and synthesizing them into tools that make the application systematic for all.
Kubernetes has become the preferred tool for DevOps engineers to deploy and manage containerized applications (including ML workloads) at scale. Observability has now become a core requirement in any production machine learning system. This comprehensive tutorial from Grafana Labs covers in detail how to introduce observability at scale in Kubernetes through the prometheus operator and beyond.
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 spoke at recently:
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