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Issue #253🤖 
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The ML Engineer this week:
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If you are a Machine Learning Practitioner looking for an interesting opportunity, I'm currently hiring for a few roles including Applied Science Manager, Applied Scientist, Analytics Team Lead, and Customer Analyst - do check it out and do feel free to share broadly!
Meta/Facebook on Towards a Real-Time Decoding of Images from Brain Activity: Meta has announced a significant development in understanding how the brain processes visual information, research which seems from scify and opening a large amount of ethical questions. Using magnetoencephalography (MEG), a non-invasive technique, they have developed an AI system that can decode and reconstruct images from brain activity in real-time. This system comprises three parts: an image encoder, a brain encoder, and an image decoder. When trained on a public dataset, the system showed that modern AI models, especially self-supervised ones like DINOv2, align well with brain signals, suggesting that these AI models learn representations similar to how the brain does. While the reconstructed images from MEG are not as precise as those from fMRI, a more spatially accurate but slower technique, they capture high-level features of the original images.
What Every Developer Should Know About GPU Computing: GPUs are designed for massive parallelism and high throughput, this article delves into key GPU concepts such as architecture, streaming multiprocessors, memory hierarchies, and CUDA. Execution on GPUs involves launching threads in grids, grouped into warps for simultaneous processing which requires a different conceptual paradigm. Dynamic resource partitioning is crucial for optimizing GPU performance, with "occupancy" measuring resource utilization. The piece underscores the importance of understanding GPUs for modern engineers, especially in the realm of machine learning.
Eugene Yan on Reflections on AI Engineer Summit 2023: At the AI Engineer Summit 2023, key takeaways included the challenges of evaluations and serving costs in deploying large language models. While evaluation techniques vary, there's no universally accepted method, though code generation offers a more straightforward evaluation path. LLMs prove cost-effective for complex tasks but are expensive for simple ones. Integration with existing systems, the potential of caching, the prominence of retrieval-augmented generation (RAG), the rise of coding assistants, and the anticipated growth of multimodal models were also discussed. The summit underscored the importance of finetuning, self-hosting, and the remarkable work ethic of San Francisco engineers. has released a multimodal foundation model available on HuggingFace under the name Fuyu-8B, which is designed to natively understand both images and text. Unlike traditional multimodal models, Fuyu-8B boasts a simpler architecture without a separate image encoder, allowing it to support arbitrary image resolutions and deliver fast response times. The model is particularly adept at interpreting charts, diagrams, and documents, and's internal versions showcase additional capabilities like OCR and UI element localization, hinting at features for their upcoming product release.
"The Startup CTO's Handbook" by Zach Goldberg is a comprehensive guide tailored for technical leaders in startups, emphasizing the importance of continuous learning and adaptability. Drawing from his experiences across various startups, it's an interesting resource that offers insights into both technical and managerial aspects of leadership. The book serves as a reference guide, covering a broad spectrum of topics from business processes to people management, underscoring the significance of viewing managers as coaches and the value of mentorship in leadership development.
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