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Issue #247
This 247 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 45,000+  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 45,000+ 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 one and only Andrew Ng shares actionable thoughts on opportunities and methodologies for successful AI opportunities in 2023 💡 In this short but insightful 30 minute session, Andrew Ng shares a conceptual foundation for his excitement behind the latest developments in AI and how they are disrupting the more traditional approaches in ML. He also covers practical examples where he has been implementing these methodologies to develop new successful startups disrupting industries through democratisation of AI-tooling that before would've otherwise be limited to top tech companies. A lot of high level learnings that machine learning practitioners can take as they are exploring emerging fields and technologies in the space.
Forecasting and time series legend Rob Hyndman is speaking this week on hierarchical forecasting accessible in an online livestream thanks to Zalando's Pricing Platform Director Tim Januschowski who is coordinating this event 🚀 This is an interesting fied of forecasting that enables the need for different levels of forecasting aggregation; for example a retail company that needs forecasts at the national-, state- and store-level for all products, for groups of products, and for individual products. You can find details of the event and how to join here and the livestream here, you can also find more information about the topic and resources here.
Glassdoor has developed and open-sourced its Machine Learning Registry as part of its commitment to becoming an ML-driven company 💡 An ML Registry is a component in the ML stack that stores model artifacts and related metadata to support consistent and reliable access to ML data across various teams and applications. Glassdoor built its registry from scratch rather than using existing solutions for reasons including complete customization, immediate support, seamless integration within the Glassdoor ecosystem, and cost considerations. The company has adopted a Git-centric approach for metadata management, this ML Registry synchronizes metadata from Git into a Redis cache and uses S3 for data storage.
The Advanced NLP Course with SpaCy: Natural Language Processing has a vast and growing number of real-world applications across academia and industry, this spaCy online course offers a deep dive into text processing, starting with foundational concepts like word and phrase identification, progressing to large-scale data analysis techniques, and delving into the intricacies of spaCy's processing pipelines. For advanced practitioners, it provides guidance on customizing and training neural network models, ensuring practitioners have the tools to tailor spaCy for specific production needs.
"Privacy Nightmare on Wheels": ‘Mozilla carried out a privacy review of car brands—Including Ford, Volkswagen and Toyota — reveals concerning findings on how 25 major car brands collect and share deeply personal data, including sexual activity, facial expressions, and genetic and health information. Notably, Nissan was identified as collecting extensive personal data without clear usage specifications. Brands like Volkswagen and Toyota also raised concerns due to their data collection and complex privacy policies. The study underscores the growing privacy challenges in the automotive sector, emphasizing the need for better data protection practices as cars become increasingly connected and data-centric.
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