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Issue #234
This 234 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 🚀
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 This week in the ML Engineer:
Thank you for being part of over 40,000+ ML professionals and enthusiasts who receive weekly articles & tutorials on production ML & MLOps 🤖 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!
The OWASP Top 10 for Large Language Model Applications project lists the most common security risks in large language models (LLMs), aiming to educate developers and organizations. This is fantastic to see as a growing trend since we published the MLSecOps Top 10 Vulnarabilities almost 2 years ago! The risks in this new resource include vulnerabilities like prompt injections, data leakage, inadequate sandboxing, unauthorized code execution, SSRF vulnerabilities, overreliance on LLM-generated content, inadequate AI alignment, insufficient access controls, improper error handling, and training data poisoning. The project is community-driven and encourages broad participation so do contribute.
It can often be hard to quantify and verbalise the business impact of technical debt 💸 This MIT paper provides a comprehensive approach to assessing the cost of Architectural Complexity,  demonstrating that it can lead to significant productivity drops, increased defect density, and higher staff turnover. The research emphasizes the importance of hierarchy and modularity in system design. For machine learning practitioners, this underscores the importance of maintaining simplicity in system architecture to optimize productivity and minimize costs.
Harvard's CS50's Introduction to Artificial Intelligence with Python 🤖 A fantastic free self-paced online course from Harvard which explores key AI concepts and algorithms such as search algorithms, knowledge representation, uncertainty handling, optimization problems, supervised and reinforcement learning, neural networks, and language understanding. The course blends theory and practice effectively, and can be accessed on Harvard's official site or edX with free resources and an optional paid certificate​.
Building MLOps at Reasonable Scale: You Don't Need a Bigger Boat ⛵ A fantastic paper which addresses the challenges of implementing recommender systems at a "reasonable scale" with a case study in the retail industry. It advocates for serverless, open-source tools to minimize infrastructure work, and proposes guiding principles for ML practitioners, including the emphasis on data quality, the separation of data ingestion and processing, and the use of platform as a service (PaaS) or function as a service (FaaS) instead of infrastructure as a service (IaaS). The paper also outlines the functional requirements of a recommender system, such as raw data ingestion, data preparation, model training, model serving, and orchestration​.
Securing AI Systems — Defensive Strategies 🥷 A great article that explores the risks and defenses associated with AI systems, focusing on intentional and unintentional failures. It emphasizes adversarial robustness as a key defensive strategy, which requires building machine learning models with strict adherence to security, privacy, and regulatory principles. This resource also dives into the challenges in implementing these defenses, including slow performance, decreased accuracy, and issues with scalability and transferability.
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.
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