650+ GenAI Case Study Learnings EvidentlyAI’s has put together over 650 case studies of real-world AI use cases and have recently published a summary of key learnings: These use-cases span across various domains including ops automation, personalization, and search, recommendations, etc. It does seem that companies are increasingly using LLMs for more complex workflow optimizations with also more sophisticated techniques, such as leveraging retrieval-augmented generation for customer support and AI agents for data access and product enrichment. There is also an interesting trend where LLMs are being used for evaluation and safety practices to improve reliability, together with other emerging trends that are certainly worth keeping a close eye! |
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The Top Languages in 2025 A new programming language rankings from IEEE Spectrum confirm Python is now at the top, however we are also realising that programming languages are also being affected by the rise of AI coding assistants: Developers are increasingly relying on LLMs instead of Stack Overflow or GitHub for software development, which is diluting traditional metrics of language use. What is more interesting is that with the rise in popularity of AI assistants, this is also having an impact on language popularity, due to the vicious circle of training data being more broadly available due to AI generation for the more popular programming languages. This is a trend that will be interesting to follow as it will also slowly also affect other domains such as the machine learning and data operations space, when it comes to languages of choice. |
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MIT on the GenAI Business Divide A recent study from MIT on the 2025 State of GenAI in Business finds that despite $30-40B invested in GenAI, 95% of enterprise pilots show no ROI: This is the elephant in the room that is starting to introduce accountability on the growing "GenAI value divide", where only ~5% is delivering direct business value. The study outlines that the gap isn’t due to model quality but to lack of model-learning, memory, and workflow integration, as well as most importantly, relevance on use-case, as often automating a small step in a larger end-to-end manual process would likely not drive significant gains. Generic tools like ChatGPT seem to boost individual productivity but have not converted into improved P&L or increase in outcomes - it will be interesting to see how this evolves once we are past the hype peak. |
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The State of MLOps 2025 Survey 🔥 We are still continuing to gather the insights on this year's MLOps Survey! We still need your support to continue collecting diverse perspectives to map the ecosystem! Please help us with your response, as well as by sharing with your colleagues 🚀🚀🚀 If you have a few minutes, your contribution will make a significant difference to the whole production ML ecosystem 🥳 The results will be shared as open source like last year!! You can add your response directly at: https://bit.ly/state-of-ml-2025 🔥 |
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Leading in a Room with Experts As a leader it is the norm to be in a room full of experts - what are then best practices to contribute as a leader in these contexts? In a team of deep experts, effective leadership is actually not about having all the answers, but about connecting the dots, translating across disciplines, keeping focus on real user problems, and framing trade-offs in terms everyone can act on. Strong leads create clarity by defining goals, adapting technical language for different audiences, and modeling humility (e.g. "I don’t know, let’s figure it out") to encourage collaboration. Similarly it is also important sometimes to think of decisions not as binary but as probabilistic bets, and sometimes unblock teams by defining a direction that may not be 100% correct but that can help move closer to the right direction. |
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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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