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Issue #242
This 242 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 πŸš€
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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 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!
"MadeWithML" is one of the most comprehensive end-to-end production machine learning course. This course is designed to guide developers in integrating machine learning into software applications. Covering topics from design to production, the curriculum emphasizes understanding ML from first principles, implementing best practices, and connecting MLOps components.
The Extended Guide for Instruction-tune Llama 2 πŸ’‘ The article offers a comprehensive guide on instruction-tuning Llama 2 from Meta AI, outlining the ability to create an instruction dataset. This dataset aids in fine-tuning Llama 2 to generate specific instructions based on input, facilitating tasks like personalized email writing. The tutorial covers defining use cases, creating prompt templates, and using TRL and the SFTTrainer for instruction-tuning, all executed on an AWS EC2 instance with an NVIDIA A10G GPU.
Going beyond Open Source to ensure transparency in large language models πŸ€– An insighful perspective that highlights the importance of AI model traceability, making the case that while open-source practices are vital for transparency, these are not the "silver bullet". The ecosystem currently has a gap on tooling required to enable the provenance of AI models, meaning the specific code and data used during training. It is proposed that to ensure a transparent AI supply chain, a combination of open-source methods and traceability tools is essential, especially given the risks associated with AI models being perceived as "black boxes" and potential hidden malicious behaviors within them.
Mind blowing text-to-music demos from META's AI Music Generation 🎢🀯 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