StackOverflow Dev Survey The Stack Overflow 2025 Developer Survey is out: AI adoption surging to 84%; however ~80% lack of trust on AI outputs. Python's share continues growing to 58% (+7% YoY) with FastAPI as the fastest growing framework (+5% YoY). Rust holds the “most loved” crown for yet another year. We're also seeing median salaries climbing slowly 5% to 29% depending on role, yet 75% of developers still find themselves unhappy/complacent, hinting at retention risks. Anthropic’s Claude Sonnet tops the admiration charts among LLMs, and the Cursor IDE already reaches 18% usage. |
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Free Databricks MLOps Course Marvelous MLOps released one of the absolute best Databricks courses on MLOps out there; and it is completely free: This is a six-part course which covers the end-to-end machine learning lifecycle in Databricks, encompassing not only the basic know-how but also best practices from real-world application. As they suggest, despite the agentic hype, most value unlocked comes from ML/DL-powered usecases, and robust MLOps can make a significant difference on scientific productivity and time-to-value. They emphasise the importance of traceability/reproducibility, Git-driven CI/CD, and monitoring (e.g. infra and data); between others. As part of the course they cover version control, orchestration, model registry, container & data versioning and observability with DBX components like Lakeflow Jobs, managed MLflow tracking/registry, serverless/cluster compute, feature store, Lakehouse Monitoring, etc. Even if you have watched some of DBX resources before, this is definitely a must watch - check it out! |
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Google DeepMind World Mapping Google DeepMind has released a planetary-scale foundation embedding model that converts petabytes of optical, radar, LiDAR and climate-simulation data into a vector space that provides signal directly instead of depending on disparate complex multi-modal datasets. It is interesting to see that whilst we've seen some previous foundation models for vertical-specific use-cases, we are now starting to see embeddings that enable broader usage; in this case it's a 1.4 trillion-vector satellite embedding that will support teams use it as a virtual-satellite feature layer for rapid enviromental ML experimentation without heavy geospatial preprocessing. |
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Multi-Generation Projects It is quite fascinating to learn about projects that were built across hundreds of years, and to think what projects to this date will be the cornerstone for the centuries to come: There are a surprisingly number of examples that went through decades and centuries, many of these are building cathedrals, long-term studies, solving scientific problems. There are some pretty interesting topics in this list, including examples such as Sagrada Familia (1882-now), Notre Dame (1163-1345), LIGO gravitational Wave detector (1967-2016-now), Japanese Company Kongo Gumi (578-2006), etc. It is interesting to imagine some of the projects that will be collectively built for the years to come, such as the Linux Operating System, or the Internet itself (protocol, infrastructure, etc). Or maybe that MLOps pipeline you built as a prototype! This is a great (albeit short) article, but definitely worth checking out. |
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ACM Transitions to Open Access Huge leap forward from the ACM making the move towards full open-access research; it's been an honour being part of the ACM Governing Board as this has been voted through! As part of this initiative ACM will make all journals / conference papers / other publications free to read by end-2025. This is definitely a huge transformation that is going the right direction, and hopefully this leap is also setting the bar for other organisations to consider open-access. This also means that with the current model authors can publish unlimited OA papers without per-article charges while retaining full Digital Library access from January 2026. Excited to continue supporting the ambition of open-access research across the ecosystem and looking forward to see it developing in the coming decade! |
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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:
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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! |
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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!
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| | | | The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning. | | | | |
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