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Issue #236
This 236 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:
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The state of production ML in the Python ecosystem πŸ’‘ As the number of production machine learning use-cases increase, we find ourselves facing new and bigger challenges where more is at stake. Because of this, it's critical to identify the key areas to focus our efforts, so we can ensure our machine learning pipelines are reliable and scalable. In this talk we dive into the state of production machine learning in the Python Ecosystem, and we will cover the concepts that make production machine learning so challenging, as well as some of the recommended tools available to tackle these challenges.
McKinsey has released an extensive report on Generative AI outlining its economic potential. The report is primarily targeted at business leaders and decision-makers who are interested in leveraging AI strategically and tactically. The report is proken down into four main chapters covering 1) Generative AI as a technology catalyst 2) Generative AI use cases across functions and industries, 3) The generative AI future of work, 5) Considerations for businesses and society. The report also outlines recommendations for business leaders, policymakers, individuals and society.
A great practical insight diving into practical LLM applications by Will Lethain, author of "The Staff Engineer". The use-case explores LLM applications using Streamlit, and demonstrates how to build interactive tools, allowing users to query OpenAI models, select a specific model, and operate on CSV files in a spreadsheet-like manner. Will concludes that personal LLM tooling could offer more customization than pre-made solutions and may represent an intriguing startup idea, despite potential challenges due to the saturation of startups in this space.
The article outlines a set of security mitigation strategies for machine learning systems based on MITRE ATLAS case studies. These strategies include the following mitigation registers: 1) limiting public release of technical information, 2) passive ML output obfuscation, 3) model hardening, 4) query restrictions, 5) access control for ML models and data, 6) ensemble methods, 7) sanitizing training data, 8) validating ML models, 9) input restoration, 10) library loading restriction, 11) encryption of sensitive data, 12) code signing, 13) verifying ML artifacts, 14) adversarial input detection, 15) vulnerability scanning, 16) strategic model distribution, and 17) user training. These techniques aim to prevent adversaries from exploiting system vulnerabilities, ensure the security of ML systems, and maintain the integrity of ML models and data.
8 annoying A/B testing mistakes every engineer should know πŸ’‘ This article highlights common A/B testing pitfalls, which encompasses: 1) including unaffected users in experiments, 2) only viewing aggregate results and neglecting subgroup insights, 3) not setting a predetermined experiment duration, 4) running full-scale experiments without preliminary testing, 5) neglecting counter metrics that measure unintended negative effects, 6) failing to account for seasonality in user behavior, 7) testing unclear hypotheses, and 8) relying too heavily on A/B tests for decision-making. This resource also highlights useful learnings, including the importance of careful experiment design, thoughtful data analysis, and considering qualitative factors along with quantitative metrics​​.
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