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Issue #260 🤖 
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This week in Machine Learning:
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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!
Practical Deep Learning for Coders is one of the staple Free courses to build a solid foundation in hands-on machine learning: Jeremy Howard's advanced 30+ hour video course targeting individuals with coding experience, focusing on applying deep learning in real-world scenarios. The course covers building and training models for various domains using PyTorch, fastai, and Hugging Face libraries, with a strong emphasis on practical skills and minimal prerequisites. Great course for ML practitioners looking to enhance their skills in model building, deployment, and understanding deep learning mechanics. It includes 9 detailed lessons teaching through hands-on exercises using tools like Jupyter Notebooks, Kaggle Notebooks, and Paperspace Gradient.
MeshGPT follows the text-to-image models with a mind-blowing text-to-3d-mesh model: MeshGPT represents a significant advancement in 3D mesh generation, utilizing a transformer-based approach to autoregressively generate high-fidelity, compact triangle meshes. This method involves learning a vocabulary of geometric embeddings, informed by local mesh geometry and topology, and using these to sequence and decode triangles. MeshGPT significantly outperforms existing methods, yielding meshes with sharp details and efficient triangulation. Quite interesting to see its applicability for tasks like shape completion and 3D asset generation, which can mark a substantial improvement in both the quality and utility of generated 3D meshes.
Google DeepMind, University of Washington, Cornell, CMU, UC Berkeley, and ETH Zurich demonstrates a novel method to extract substantial amounts of training data from ChatGPT: An insightful research paper that re-emphasises the importance of security in production machine learning system. In this paper they show how prompting ChatGPT to repetitively use a word (e.g., "poem"), they bypassed its alignment safeguards, revealing sensitive information and highlighting significant vulnerabilities in production models. This emphasises the importance of patching specific exploits and addressing underlying vulnerabilities. The findings emphasize the importance of understanding and mitigating fundamental vulnerabilities in language models, contributing to the field's knowledge of securing machine learning systems in production environments.
Generating Multi-View Optical Illusions with Diffusion Models: A novel zero-shot method for generating multi-view optical illusions using pretrained diffusion models. This method estimates and averages the noise in different image transformations, such as rotations, flips, and color inversions, using a diffusion model. The approach requires the transformations to be linear and statistically consistent, with a focus on orthogonal transformations like pixel permutations. This advancement in creating optical illusions demonstrates a unique application of diffusion models, offering significant improvements over previous works in terms of illusion quality and range of transformations, and has potential applications in artistic and visual media domains.
Challenges & Decisions of Designing a Distributed SQL Engine: An insightful resource discussing the intricacies of designing an SQL engine, which focuses on the architecture decisions, including the Plan Cache, Query Optimizer, and various execution engines (Volcano, Parallel, Vectorized). Some of the key highlights include the efficiency of Plan Cache in OLTP workloads, the use of the System-R approach in the Query Optimizer, and the distinct features of each execution engine to optimize SQL query processing. This information is particularly relevant for MLOps / ML Platform practitioners dealing with large datasets, as it offers insights into efficient data querying, optimization techniques, and execution strategies crucial for managing and processing big data in machine learning pipelines.
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!
© 2023 The Institute for Ethical AI & Machine Learning