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Issue #230
This 230 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 30,000+  subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions πŸš€
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 This week in the ML Engineer:
Thank you for being part of over 35,000+ ML professionals and enthusiasts who receive weekly articles & tutorials on production ML & MLOps πŸ€– If you havent, you can join for free at ⭐
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!
This MLOps guide by Claypot AI Founder Chip Huyen is a fantastic resource for machine learning practitioners at all levels πŸ’‘ This resource covers foundational machine learning concepts, offers an in-depth exploration of MLOps, and provides career development advice. Additionally, it includes real-world case studies from companies like Airbnb, Netflix,, and Uber to demonstrate practical applications of machine learning in production environments. The guide concludes with miscellaneous resources such as coding exercises and Python tips. It serves as a valuable tool for anyone working in machine learning production.
AI Prompt Injection explained, with video, slides, and a transcript πŸ’‰Insightful resource which discusses the security vulnerability of prompt injection in applications built on AI models. Prompt injection involves injecting unauthorized instructions into user input to manipulate an AI system's behavior. The potential implications are significant, including AI assistants being manipulated to leak confidential information. While the solutions are complex and challenging to implement, this resource emphasizes the importance of community awareness, discussion, and research to combat these vulnerabilities.
Binance on Why and How they use real-time Machine Learning to monitor fraudulent activity πŸͺ™ Binance uses real-time machine learning to detect and prevent fraudulent activities on their cryptocurrency platform. To counter "model staleness" where predictions become inaccurate over time, they employ both batch and streaming data pipelines, with a particular emphasis on real-time (streaming) data. Their real-time machine learning pipeline comprises data processing and data serving components, incorporating stream computing, ingestion, and sinking for data processing and online prediction, and batch computing for data serving. Balancing data freshness and latency, the system supports continuous monitoring and protection in the 24/7 crypto market.
Prime Video significantly scaled up its audio/video monitoring service and reduced costs by 90% by transitioning from a distributed microservices architecture to a monolithic application πŸ’Έ The original serverless design led to scaling bottlenecks and high costs due to expensive AWS orchestration workflows and data transfer between distributed components. The new monolithic architecture, running on Amazon EC2 and Amazon ECS, simplified orchestration, eliminated the need for intermediate data storage, and improved scaling capabilities. This change also enhanced the overall customer experience by enabling monitoring of all streams. The experience highlights the importance of selecting between microservices and monolith architectures based on specific use-cases.
The Carnegie Mellon University (CMU) Database Group YouTube
channel offers valuable content on database-related topics, making it a great resource for software, data & machine learning practitioners πŸ€– It features playlists on Database Systems, Data Mining, Big Data, Web Search, and Database Applications, covering a broad spectrum of topics from data modeling to big data tools. Understanding databases can enhance the efficiency of data-intensive and machine learning applications, from storing training data to holding prediction results.
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.
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!
Β© 2018 The Institute for Ethical AI & Machine Learning