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Issue #257 🤖 
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The ML Engineer this week:
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TimeGPT introduces magical auto-forecast capabilities that leverage GPT breakthroughs, making advanced forecasting accessible to anyone. The team behind the highly popular open source packages statsforecast, neuralforecast and mlforecast have released TimeGPT, a cutting-edge generative pre-trained transformer model dedicated to forecasting tasks. This showcases innovative application of SOtA technology in a traditional field of ML, allowing users to benefit from forecasts with a handful lines of code, automating standard tasks with best practices across cleaning datasets, perform hyperparam optimisation, cross validation, as well as concepts such as seasonality, trends, uncertainty estimation, etc.
When UI/UX design and AI intersect we get mind-blowing results: In this case TLDraw is bridging design and implementation by introducing a ChatGPT powered tool that converts sketches into real-life web applications that can be iterated. Their free OSS tool is live at, where users can sketch a user interface and convert it into a functional website using OpenAI's GPT-4 with Vision model. This tool, leveraging the tldraw component, enables an interactive and iterative design process directly on the canvas, where users can draw, edit, and refine their UI designs with AI assistance. This development captures the significant leap we are seeing in in AI-assisted web design, offering practitioners a novel and efficient method for rapid prototyping and development of web interfaces.
DeepMind on a new Weather forecasting Deep Learning model: GraphCast is a groundbreaking AI model for weather forecasting, outperforming traditional systems with its ability to deliver highly accurate 10-day forecasts in under a minute. Utilizing machine learning and Graph Neural Networks, and trained on four decades of weather data, GraphCast excels in predicting various atmospheric and Earth-surface variables, including extreme weather events. Its efficiency and accuracy, demonstrated in surpassing over 90% of traditional forecast variables, mark a significant advancement in weather prediction technology. What is best is that it has been Open-sourced for broader use; GraphCast represents a major leap in AI's application to environmental challenges, and suggests offering both practical forecasting tools and insights into climate pattern understanding.
A new free online course on Deep Learning by the University of Geneva, Switzerland: This course covers a wide range of topics, including machine learning fundamentals, tensor operations, deep learning techniques, and applications in computer vision and natural language processing, using the PyTorch framework. The course materials, including detailed slides, handouts, and screencasts, are well-suited for professionals seeking to deepen their understanding of modern deep learning concepts and practices. Additionally, it provides practical sessions and a virtual machine setup for hands-on experience, although with noted performance and security limitations.
Following the broad range of benchmarks across transformer-based LLMs, we now see are starting to see evaluations that compare human performance across relevant tasks. This insightful paper examines the abstract reasoning capabilities of GPT-4 and its multimodal variant, GPT-4V, using the ConceptARC benchmark. It extends previous research by applying more complex one-shot prompts to GPT-4 and evaluating GPT-4V with both zero- and one-shot prompts on simpler tasks. The findings reveal that neither version of GPT-4 currently matches human-level abstraction abilities, offering insights for machine learning practitioners in AI and language model 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!
© 2023 The Institute for Ethical AI & Machine Learning