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Issue #237
This 237 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 🚀
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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 UK Government has adopted 3 of the 4 recommendations we made for the initial UK AI Regulation proposal - we are now thrilled to announce we have submitted further recommendations as part of the current open policy consultation for the UK AI Regulation Proposal 🚀 The adopted recommendations include 1) Technological Interoperability, 2) Comprehensive Definitions, 3) Transparent Process. The recommendation on 4) Environmental Risks" has now been emphasised and extended. Further ecommendations are now made including 5) encompassing matters of legitimacy" and "contestability", 6) align definition of AI with OECD and European Commission, 7) machine learning security to be aligned with the UK NCSC, 8) informatics education be promoted, 9) "open models" to be considered, and 10) the UK's risk management framework align with the EU's.
New course from Andrew Ng's DeepLearning.AI in collaboration with AWS on Generative AI 🚀 This free course is aimed at machine learning practitioners seeking a comprehensive understanding of generative AI. The course covers key aspects of generative AI, including data gathering, model selection, performance evaluation, deployment, and how to adapt and optimize models to various use cases. The course draws on the latest research and offers insights from industry practitioners, preparing learners to implement generative AI in real-world applications.
Building MLOps at Reasonable Scale: You Don't Need a Bigger Boat ⛵ A fantastic github repo providing a practical implementation to the previously shared paper on MLOps at Reasonable scale. This resource addresses the challenges of implementing recommender systems at a "reasonable scale" with a case study in the retail industry. This repo provides an implementation of an end-to-end MLOps pipeline with: 1) Metaflow for ML DAGsSnowflake as a data warehouse solution (Alternatives: Redshift), 2) Prefect as a general orchestrator (Alternatives: Airflow, or even Step Functions on AWS), 3) dbt for data transformation, 4) Great Expectations for data quality (Alternatives: dbt-expectations plugin), 5) Weights&Biases for experiment tracking (Alternatives: Comet, Neptune), and 6) Sagemaker / Lambda for model serving.
Stanford has put together an informative repo which introduces the "Foundation Model Transparency Index"; a framework which rates major AI providers like OpenAI and Google on their adherence to requirements specified in the EU Parliament's draft AI Act. After identifying 22 relevant requirements from the AI Act, the authors evaluated 12 that apply directly to foundation model providers, using a custom 5-point rubric for each. The resulting ratings aim to foster greater transparency and accountability among AI model providers and ensure alignment with ethical and user protection principles.
Dealing with Train-serve Skew in Real-time ML Models: A Short Guide 🔭 A comprehensive article that demystifies the training-serving skew on ML models, which arises due to differences between training and serving environments in real-time machine learning models. To mitigate this issue, the guide recommends monitoring and debugging discrepancies, particularly with high-importance features, by collecting and comparing data from both training and serving paths. Other suggested strategies include using a feature store for feature calculation, maintaining clear communication between data scientists and machine learning engineers, and deploying models in "shadow-mode" to test for train-serve skew without impacting business operations.
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!
About us
The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning.
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