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Issue #225
This 225 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 30,000+  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:
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Stanford releases the annual AI Index Report πŸ’‘ The AI Index Report is an annual report that tracks and visualizes data related to artificial intelligence with the aim of enabling decision-makers to advance AI responsibly and ethically with humans in mind. The 2023 report indicates that industry has overtaken academia in producing significant machine learning models, and AI is both helping and harming the environment. The demand for AI-related professional skills is increasing across virtually every American industrial sector, and policymaker interest in AI is on the rise. Additionally, the number of incidents concerning the misuse of AI is rapidly rising, and it highlights that Chinese citizens feel more positively about AI products and services than Americans do.
The article announces the release of a new course, "From Deep Learning Foundations to Stable Diffusion," which is part 2 of Practical Deep Learning for Coders. The course covers over 30 hours of video content and includes the implementation of the Stable Diffusion algorithm from scratch, along with other diffusion methods. The course also covers essential deep learning topics such as neural network architectures, data augmentation approaches, loss functions, and deep learning optimizers, among others.
The article highlights 9 top machine learning papers that production machine learning practitioners should read in 2023, covering a range of topics such as generative models, time series analysis, optimization algorithms, synthetic data generation, natural language processing, text-to-video generation, and workflow efficiency. The papers include research on neural singing voice beautification, a new optimization algorithm for neural networks, a method for transforming 1D time series data into 2D data, an open pre-trained transformer language model, a model for generating realistic relational and tabular data, benchmarks for natural language policy optimization using reinforcement learning, a method for tuning text-to-video generation, and a library for efficiently sharing machine learning ideas.
OpenAI publishes their approach to AI Safety πŸ€– This approach encompasses rigorous testing, engaging external experts for feedback, building monitoring systems, and using reinforcement learning with human feedback to improve the model's behavior. OpenAI outlines the importance of substantial safeguards to make continuous improvements based on lessons learned from real-world use. There is also emphasis on protecting children, respecting privacy, and improving factual accuracy.
Facebook's MLOps Ecosystem
Facebook/Meta shares their approach to organisation-wide MLOps πŸ’‘ Facebook/Meta has developed measurement processes to manage AI models effectively and efficiently, and shares techniques that can be applied broadly in other organizations. They discuss the goals and principles of AI model management, Meta's ML-Ops ecosystem, and the importance of consistently defining key concepts in AI model management. The taeam also emphasize the need for a clear metadata architecture to bridge specific system implementations via common labels. If you are interested in the topic you can check out the recording of our talk on Metadata Systems for End-to-End Data & Machine Learning at PyData Global 2022.
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.
Other 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