Ruy Ley-Wild

Gheorghe Comanici, Eric Bieber, Mike Schaekermann et al. · amazon-science, baidu

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving.

Nantia Makrynioti, Ruy Ley-Wild, Vasilis Vassalos

We present sql4ml, a system for expressing supervised machine learning (ML) models in SQL and automatically training them in TensorFlow. The primary motivation for this work stems from the observation that in many data science tasks there is a back-and-forth between a relational database that stores the data and a machine learning framework. Data preprocessing and feature engineering typically happen in a database, whereas learning is usually executed in separate ML libraries. This fragmented workflow requires from the users to juggle between different programming paradigms and software systems. With sql4ml the user can express both feature engineering and ML algorithms in SQL, while the system translates this code to an appropriate representation for training inside a machine learning framework. We describe our translation method, present experimental results from applying it on three well-known ML algorithms and discuss the usability benefits from concentrating the entire workflow on the database side.