How time flies!! We are celebrating 6 years since starting this weekly MLE newsletter 🎉🎉🎉 What started with just one commit, today now has almost 70,000 subscribers 🚀 And not a single Sunday missed 🤯 Thank you to everyone for your continued support - in today's newsletter we share a special edition celebrating our achievements throughout 2023! |
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Databases Year in Review 2024 2024 Database year in review: Snowflake vs Databricks! Open Source vs Commercial! DuckDB vs the world! Who is going to win?? The database ecosystem had a lot of interesting developments throughout 2024. Some of the key highlights: 1) disruptive license changes (e.g., Redis and Elasticsearch), 2) the escalating Databricks–Snowflake rivalry, 3) LLMs and all-things RAG, and 4) DuckDB’s rise as a lightweight analytical engine embedded in other systems. This is a great introspective year-in-review for 2024 from CMU, check it out! |
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Crunching Data Anywhere with DuckDB DuckDB is the database that even data scientsits want to love: crunching data anywhere, from laptops to servers: DuckDB is an OSS in-process SQL database for intuitive data-workdlows which can be embedded directly into your Python, R, or other jupyter environments. This is a fantastic video from GOTO Conf that deeps dive on some of the designs that are fueling DuckDBs rise to popularity: columnar storage, vectorized execution, and built-in support for common data formats (CSV, Parquet, JSON). DuckDB runs inside your application process which means that it's as easy to leverage as Pandas for data processing, but enabling for terabyte-size processing. If you haven't yet heard of DuckDB this is a great resource to dive into it and get an intuition on the potential opportunity in this space! |
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Learnings from LLMs in 2024 Django Co-creator on things we learned about LLMs in 2024: As always really insightful snippets from Simon Willison, this time doing a 2024 year-in-review on LLMs. Key lessons: 1) The “GPT-4 barrier” was shattered by multiple labs; 2) Multimodal capabilities; 3) Limitations on “agents” with actual autonomy; 4) Rigorous automated evaluations; 5) Apple Intelligence's disappointments; 5) Concerns on environmental impact; 6) + more. |
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Evolution of SRE at Google As organisations grow their Machine Learning capabilities, there are key learnings that we can take from the SRE space: this is a great write-up on how SRE has evolved at Google beyond traditional reliability tools (like SLOs, error budgets, and postmortems) to address the complexity of modern systems, especially AI-driven ones where certain failures (e.g., privacy breaches) cannot be allowed. This actually has quite an interesting overview on adopting "STAMP" (System-Theoretic Accident Model and Processes), which then Google renamed these as control problems which shift the focus to the broader interactions among system components. This is quite an interesting approach which ensure prevention of catastrophic failures - certainly worth taking note for MLOps and ML Engineering practitioners. |
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The Quiet Chinese AI Giant Deepseek AI took the world by surprise with a foundation model from China that surpassed all models - this is quite an interesting fire-side chat with the founder Liang Wenfeng (translated from chinese): Deepseek AI is a Chinese AI startup backed by a massive eastern hedge fund, which has quietly gathered what seems to be pretty massive compute resources, and most interestingly non-NVIDIA GPUs, wich possible 50k "Hopper" GPUs. As part of their release they also introduced some innovative approaches to their foundation model, with multi-head latent attention and sparse MoE, which ended up beating OpenAI’s o1 on multiple benchmarks. What is most interesting is that this has now triggered a nationwide price war with ultra-cheap inference costs to enter the global AI race. This is certainly an interesting space as other nations enter the race of foundation models, which are driving further innovation and breakthroughs in the field - certainly a space to continue keeping an eye to. |
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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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© 2023 The Institute for Ethical AI & Machine Learning |
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The Institute for Ethical AI & Machine Learning
