This coming week I'll be in Berlin speaking at the WeAreDevelopers Summit 2024 with other incredible speakers such as StackOverflow CEO, Atlassian CTO, SAP CTO, Github CEO, Former Apple COO + many more! I'll be giving a talk on "The State of Production ML in 2024", if you're around come say hello 👋! |
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Building Notion's Data Lake Notion shares an insightful peek into their massive data growth, covering lessons learned and best practices building their org-wide data-lake: During their large-scale data journey Notion transitioned from a single Postgres instance to a sharded architecture and built an in-house data lake using S3, Kafka, Debezium, and Apache Hudi. This infrastructure was introduced to reduce costs and data ingestion times, whilst supporting update-heavy workloads and enabling advanced AI and Search features without fully replacing existing solutions like Snowflake and Fivetran. |
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RouteLLM for Effective GenAI The retrieval-augmented-generation innovations continue to grow, recently we saw RouteLLM as an alternative architecture toto reduce the costs of deploying large language models by routing queries between high-performance/expensive models and smaller/cheaper ones based on query complexity. RouteLLM achieves significant cost savings (up to 85%) while maintaining up to 95% of the performance of the top models like GPT-4 by utilizing preference data and various machine learning techniques. |
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Goldman on GenAI Value Gap Goldman Sachs talks about the GenAI elephant in the room, point out to the estimated ~$1tn AI capex spend from tech companies on GenAI and foundation models with the key need to see results: Goldman brings pragmatic insights on the opportunities and gaps in the AI gold rush, covering even the already-known supply constraints in AI chip production which is expected to lag with shortages beyond 2025. Even then, investment phases indicate immediate gains for companies like Nvidia producing, closley with infrastructure firms following - both which are bringing the "pick-axes in the gold rush". Even with that in mind, there is a stern warning on economic risks due to high valuations, highlighting the obvious on the remaining need to see substantial productivity gains from AI to show the potential across the S&P 500 and beyond. |
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How to Interview ML Engineers Eugene Yan on how to effectively hire ML/AI engineers: A great resource covering best practices on MLOps recruitment, emphasising the importance of technical skills like software engineering, data literacy, and model evaluation, as well as non-technical traits such as handling ambiguity, influence and complexity. There are best practices from standard software interviews that can be leveraged, such as structured interviews using the STAR format, technical phone screens, and detailed debriefs to make informed decisions. |
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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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The Institute for Ethical AI & Machine Learning
