Tech University Munich AI Talk What are the most pressing issues on ML Governance? What about the most important concepts of Responsible AI? Join us next week to find out, as we'll be joining the Technical University Munich's Institute for Ethics in AI to dive into the topic of "Responsible AI in 2024": In this session we will explore the current landscape of responsible AI, focusing on the industrial, organizational, and technical aspects crucial for successful AI deployment, including governance challenges, accountability, security concerns, infrastructure complexities, risk mitigation, and building scalable, reliable AI systems that drive innovation while adhering to responsible practices. |
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 | Most Popular ML Training Tools Which do you think are the most popular frameworks in production ML? Tensorflow is a tool of the past with only 8%! Sklearn reaches the top with 35% closely followed by Pytorch with 32% - other contenders are XGBoost with 7% and Catboost with 6% 💻 We are uncovering great insights as part of our survey on The State of Production ML in 2024; please contribute to this valuable investigation on machine learning tools and platforms used in your production ML development. Your input will help create a comprehensive overview of common practices, tooling preferences, and challenges faced when deploying models to production, ultimately benefiting the entire ML community 🚀 |
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McKinsey AI Power Capacity McKinsey dives into the explosive growth of generative AI has been leading to a significant capacity shortfall due to the growing demand of AI data centers requiring global capacity to triple by 2030: An interesting analysis diving into how this surge has been driven primarily by hyperscalers hosting advanced AI workloads, and which limit computational resources for production machine learning practitioners, which requires the need to adapt strategies for efficient resource utilization. |
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Superlinked on Vector Indexes Vector indexing techniques are making strides on bridging the gap between traditional RDBMS and Vector Databases: It is interesting to see how we're revisiting the foundations of databases with recent innovations on efficiency of similarity search in high-dimensional data for production machine learning applications which is becoming critical for Retrieval Augmented Generation. Superlinked dives into various approaches for indexing methods to address foundational challenges, such as Inverted File Indexing where clusters data points using K-means clustering. It is quite refreshing to see simple coding examples illustrating complex topics, particularly in this case showcasing how similar indexing techniques can help optimize search performance. |
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OpenAI Doubling Down Search Recently we have seen OpenAI exploring other avenues for innovation, most recently taking the search Giants by positioning ChatGPT powered products to tackle web search requirements: Quite interesting to see innovations in the space of web search, particularly in context of fine-tuning LLMs (aka GPT-4o) for conversational answers enriched with up-to-date information and direct links to relevant web sources. Only time will tell whether this avenue will be able to properly challenge the search giants in their own game, or whether this will fall under an interesting but limited set of features for information retrieval. |
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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 2024:
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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