Github CEO on AI Code Race GitHub CEO dives into the Coding Agent race and the trends we are seeing across Cursor, Windsurf and others: It is quite interesting to see how Github was able to see to get ahead of the race with copilot but now struggle with homogenisation of features. It seems there's still some moat with GitHub Actions in CI/CD pipelines as well as the agent VS Code / gh issue interface but it seems the race is only picking up pace. This is quite an insightful podcast that covers some of the ongoing trends on coding agents, as well as limitations and emerging opportunities, definitely worth checking out. |
|
|
|---|
|
Black Forest Labs Flux.1 Kontext Black Forest Labs releases a new image-workflow model for interactive modification of images through text: FLUX.1 Kontext is a suite of generative flow-matching models that unifies text-to-image and image-to-image workflows by accepting both text prompts and input images to perform context-aware generation and editing without any fine-tuning. It's quite impressive to see what it can do across elements of an image, preserving the rest of the scene whilst modifying only what is requested via text / image. This is certainly a space that is only speeding up, and new creative features are coming out almost every week changing the field for players trying to innovate. |
|
|
|---|
|
The Hidden Bloat on ML Systems Do you know about the hidden bloat in machine learning libraries? It is interesting to see that across ML frameworks there is a huge amount of shared library size through GPU kernels and other components. In experiments across PyTorch, TensorFlow, vLLM, and Transformers for models from MobileNetV2 to Llama-2-7B there is an opportunity to optimize shared‐library size by roughly 47–55%, prunes up to 72% of CPU code and 75% of GPU code, and yields up to 74% lower CPU memory, 70% lower GPU memory, and ~45% faster startup. This is quite an important paper to bring optimizations beyond purely compute into the frameworks themselves, and hopefully raising the bar for standardisation. |
|
|---|
|
OpenAI ChatGPT Academy OpenAI has released the ChatGPT Academy with end-to-end courses across a broad range of agentic tasks: The are quite a lot of new courses like "ChatGPT at Work", which is a set of of short video modules and webinars that equip ML teams to integrate ChatGPT into real‐world systems. It begins with foundational overviews of GPT models and prompt engineering (from basic to advanced), then delves into reasoning techniques and multimodal inputs, shows how to build retrieval‐augmented pipelines (ie ChatGPT Search) and automated data analysis, and includes guidance on deep research workflows and hands‐on project examples. There are also longer ChatGPT webinars and popular tutorials (e.g., Codex setup, RAG with GitHub) to help you accelerate your workflows. |
|
|
|---|
|
Introducing Perplexity Labs Perplexity has launched Perplexity Labs extending DeepResearch capabilities with interactive multi-step workflows that generate complete deliverables such as code-driven data prototyping, charts, reports, spreadsheets, and even interactive mini-web apps: It is quite exciting to see some of these type of developments with a new service that uses deep web browsing, code execution, and asset management for creating end-to-end projects and apps as the outcome. It seems that this may become a new trend, and wouldn't be surprised if we see releases from across the other research labs on extremely similar releases (with slightly different namings). |
|
|
|---|
|
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. Upcoming conferences where we're speaking:
Other upcoming MLOps conferences in 2025:
In case you missed our talks:
|
|
|---|
| | |
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!
|
|
|---|
| | |
| | | | The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning. | | | | |
|
|
|---|
|
|
This email was sent to You received this email because you are registered with The Institute for Ethical AI & Machine Learning's newsletter "The Machine Learning Engineer"
|
| | | | |
|
|
|---|
|
© 2023 The Institute for Ethical AI & Machine Learning |
|
|---|
|
|
|
The Institute for Ethical AI & Machine Learning
