This coming week I'll be in San Francisco speaking at the Data & AI Summit with other incredible speakers such as NVIDIA's Jensen Huang, Stanford's Fei-Fei Lee, and many more! I'll be giving a talk on "Building a multi-petabyte data platform at Zalando", if you're around come say hello 👋! |
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Lessons from a year of LLM Apps What We’ve Learned From A Year of Building with LLMs - a comprehensive deep dive from Eugene Yan, Hamel Husain et. al: Building robust products in production that leverage LLMs remains challenging, but there are lessons learned from success stories. Some key insights include the importance of prompt engineering, structured outputs, and retrieval-augmented generation (RAG) to improve performance. Operationally it is key to consider data consistency, version control, and potentially using smaller models to optimize costs. Effective evaluation, monitoring, and involving design early in the process are crucial as well throughout the e2e lifecycel, as well as "focus on processes over tools" to empower diverse roles within the team for successful execution. |
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Andrew Ng on Real-World GenAI AI legend Andrew Ng discusses with Ben Lorica Generative AI in the Real World and where it is headed: A great podcast which dives into key aspects of production GenAI, such as the importance of scaling AI through agents, which enable LLMs to act as autonomous systems that can iteratively plan and execute tasks, which has the potential "to go beyond Process Automation". There is also interesting discussion on the potential need for new hardware for AI inference, as well as the potential for what is referred to as "agentic moments" where LLMs can operate fully autonomously. |
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McKinsey State of AI Report McKinsey has released the State of AI in 2024 report with insightful metrics on AI in industry: The adoption of generative AI (gen AI) has surged with 65% of organizations now using it regularly, which is leading to significant business benefits such as cost reductions and revenue growth, particularly in marketing, sales, and product development. AI usage has increased globally with 72% of organizations implementing it across multiple functions. Investment in AI technologies is only growing - however, risks like inaccuracy and cybersecurity are also only on the rise. There are also insights from high-performing organizations using GenAI growingly/extensively, and attribute over 10% of their EBIT to AI (although IMO these days everything seems to fall on the AI bucket when it's not). |
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Tech Managers Anti-Patterns Unexpected Anti-Patterns for Engineering Leaders — Lessons From Stripe, Uber & Carta: CTO at Carta Will Larson shares three "unconventional anti-patterns" in engineering leadership: avoiding micromanagement, resisting flawed metrics, and shielding teams from problems. Some interesting insights for senior tech leaders, such as engaging deeply in team conflicts, documenting strategies thoroughly, using imperfect metrics for learning, and involving teams in decision-making. |
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Japan's Push for Open Research Japan is launching a national initiative to make all publicly funded research open access by 2025, investing ¥10 billion to standardize institutional repositories across universities: A fantastic initiative which hopefully is only a growing trend, with Japan's plan focusing on open access, where author-accepted versions of research papers are freely available, which would truly drive research collaboration. This move aims to address Japan's declining international research standing, but is a great step forward - other organisations like the ACM are investing in open access initiatives but it's great to see initiatives at the national level. |
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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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