Building a Multi-Petabyte Data Platform Our talk from the Data & AI Summit is Live! Check out our overview of Zalando's journey "Building a Multi-Petabyte Scale Data Platform" 🚀 At Zalando, data is at the core of everything—as the organization has grown to 25 markets, 50M+ active customers, 1.8M+ articles and ~20K employees so has the multi-petabyte-scale data & AI opportunities. This talk provides a deep dive into the historical evolution of the Zalando data platform, an dives into some of the challenges and opportunities that it has unlocked. This is presented from the perspective of both, the central infrastructure and the data owning organisations, showcasing the importance of strong alliances between these for long term success. |
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Chip Huyen on AI Engineering Join AI founder & Author of O'Reilly Book "Designing ML Systems" Chip Huyen at this upcoming ACM fireside chat discussing practical advice on "AI Engineering" moderated by IEML Founder and Zalando Director Alejandro Saucedo. This session will explore the unique challenges of productionizing foundation models compared to traditional machine learning models. Despite sharing some core principles, foundation models introduce new complexities due to their open-ended nature, advanced capabilities, and computational demands. Key changes include shifting from closed-ended to open-ended evaluation, from feature engineering to context construction, and from structured data to unstructured data. This will be a great session so don't forget to RSVP! |
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META Multi-Modal Architecture Meta releases Chameleon, a new multi-modality architecture that aligns text and image inputs through unified tokenisation as opposed to separate model architectures. Huge props for another open release with the open model weights, including the 7B and 34B models (albeit under a research only license). This includes a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence, which suggests to match or exceed the performance of much larger models, including Gemini Pro and GPT-4V |
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Google Audio Gen Model Google releases an interesting new model that generates audio from videos - although counterintuitive, this seems to open a lot of interesting opportunities across both real-world application, as well as research. Google showcases how they experimented with autoregressive and diffusion approaches to discover the most scalable architectures and ensuring synchronisation video and audio. Google also highlights interesting investments on safety with a "watermark tookit" that aims to safeguard against misuse, which they will further evaluate before opening. |
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Distributed Systems Fun & Profit Distributed systems are a fundamental concept to learn for practitioners in the DataOps and/or MLOps space; this free online book provides a great deep dive into key topics in distributed systems. This includes basic fundamentals such as scalability, availability, perfrmance, latency and fault tolerance. It also dives into key concepts such as abstractions, CAP theorem, time & order, and replication. For practitioners that are keen to dive deeper, it's always a great recommendation to check out the classic "Designing Data Intensive Systems" from O'Reilly. |
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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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