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Issue #235
This 235 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 40,000+  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 40,000+ 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!
Machine Learning System Security: Risks & Best Practices πŸ₯· The Linux Foundation has published a fantastic writeup from our NeurIPS workshop keynote last year on ML Security, which showcases key vulnerabilities throughout the ML Lifecycle as the "MLSecOps Top 10", together with best practices to mititage them. This is critical due to the inherent complexity and nuanced security risks in ML systems. Mitigation strategies involve artifact signing, adversarial detectors, code reviews, secure deployment practices, and enhanced dependency management. Beyond tool usage, a holistic approach considering infrastructure security, access control, encryption, and pipeline hardening is needed, as well as effective collaboration among various teams is essential and staying updated on the latest ML security developments is also crucial
Google has launched the Secure AI Framework (SAIF), aiming to set security standards for responsible AI development and deployment πŸ’‘ SAIF is designed to mitigate AI-specific risks, such as model theft and data poisoning, inspired by established best practices from software development. The framework has six core elements, which include: 1) extending strong security foundations to the AI ecosystem; 2) incorporating AI into organizational threat detection and response; 3) automating defenses; 4) harmonizing platform-level controls; 5) adapting controls for faster feedback loops during AI deployment; and 6) contextualizing AI system risks in business processes. Google is actively fostering support for SAIF and plans to release open-source tools to help implement its elements
Disentangling and Operationalizing AI Fairness at LinkedIn 🀝 This insightful paper from Linkedin R&D presents a comprehensive approach to integrating fairness into AI systems, using LinkedIn as a case study. The authors propose a framework that considers fairness throughout the AI product lifecycle, from problem formulation to model training, evaluation, and deployment. They emphasize the importance of defining fairness metrics, using fairness-enhancing interventions during model training, and conducting thorough evaluations before and after deployment. The paper also highlights the potential unintended consequences of fairness interventions and encourages ongoing monitoring and collaboration to address AI fairness challenges.
META's AI Music Generation + mind blowing text-to-music demos 🎢🀯 This paper introduces MUSICGEN, a novel single-stage transformer Language Model for conditional music generation. Unlike previous models, MUSICGEN operates over multiple streams of compressed discrete music representation, eliminating the need for cascading models and enabling high-quality music generation conditioned on textual description or melodic features. The authors also present a chromagram-based conditioning method to preserve the melodic structure during music generation. It has been fantastic to see the surge of models for music generation take off following Google's MusicLM, creating new opportunities across research and industry.
The insightful article discusses Google's method to understand and reduce technical debt. Motivated by their quarterly engineering satisfaction survey results, Google identified ten categories of technical debt from interviews with experts. Despite unsuccessful attempts to develop predictive metrics from log data, they continued to measure technical debt via their survey. Google also created a technical debt management framework, organized educational courses, and provided tools to help teams identify and manage technical debt. These efforts resulted in a significant reduction in technical debt, with most Google engineers reporting minimal or no hindrance from it.
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!
Β© 2018 The Institute for Ethical AI & Machine Learning