LLM

The Lakehouse architecture has been successfully applied to sports intelligence, transforming raw tracking data into actionable insights that provide a competitive advantage for teams. By leveraging the Lakehouse's unified data storage and analytics capabilities, teams can now analyze vast amounts of data, including 20,000+ data points per second, to gain a deeper understanding of player and team performance. This enables data-driven decision-making, improved player development, and enhanced fan engagement. As a result, teams can now turn data into a key differentiator in the competitive sports landscape.

Perceptron Mk1, a highly performant video analysis AI model, achieves 80-90% cost savings compared to Anthropic, OpenAI, and Google's offerings, while delivering robust video understanding capabilities. This model can process live feeds with high accuracy, making it suitable for security, surveillance, and content moderation applications. The practical implication for engineers building AI systems is the potential to deploy high-quality video analysis capabilities at a significantly lower cost. Perceptron Mk1's efficiency and cost-effectiveness make it an attractive solution for enterprises and organizations seeking to leverage AI-driven video analysis.

A team of engineers leveraged large language models (LLMs) to create a working fitness app in 4.5 hours, showcasing the potential of LLM agents in rapid application development. This achievement demonstrates the feasibility of using LLMs for spec-driven development, where the model generates code based on high-level specifications. The practical implication for engineers building AI systems is the ability to accelerate development cycles and reduce the time-to-market for applications.

In this post, we show you how to set up FLOPs tracking during LLM fine-tuning using the open source Fine-Tuning FLOPs Meter toolkit on Amazon SageMaker AI. You learn how to determine your compliance status with a single configuration flag and generate audit-ready documentation....

Large language models (LLMs) now power everything from customer service bots to autonomous coding agents....

Is AI leaving the era of "turn-based" chat? Right now, all of us who use AI models regularly for work or in our personal lives know that the basic interaction mode across text, imagery, audio, and video remains the same: the human user provides an input, waits anywhere between milliseconds...

A doctor in a hospital exam room watches as a medical transcription agent updates electronic health records, prompts prescription options, and surfaces patient history in real time. A computer vision agent on a manufacturing line is running quality control at speeds no human inspector can match. Bot...

Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is often closely related to token usage....

A practitioner's argument that meeting summarizers fail in the same way regressions fail when you skip the part where you ask what the data can support. The post LLM Summarizers Skip the Identification Step appeared first on Towards Data Science ....

From tokenisation to evaluation : how modern language models actually work in practice The post The Must-Know Topics for an LLM Engineer appeared first on Towards Data Science ....

TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems....

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A learning-oriented workflow for understanding new open-weight model releases...

How coding agents use tools, memory, and repo context to make LLMs work better in practice...

From MHA and GQA to MLA, sparse attention, and hybrid architectures...

This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model....