Free hands-on "LLM From Scratch" course that builds a tiny LLM from nothing to a working model. It comes in six parts: tokenization, transformer, training loop, generation, scaling experiments, and a ...

Many in the industry think the winners of the AI model market have already been decided: Big Tech will own it (Google, Meta, Microsoft, a bit of Amazon) along with their model makers of choice, ...

A new tool from Microsoft aims to bridge the gap between application development and prompt engineering. Overtaxed AI developers take note. One of the problems with building generative AI into your ...

Strategic AI deployment could unlock $4.4 trillion in productivity growth, yet only 1% of leaders consider their companies AI-mature, according to a McKinsey report. A key part of reaching maturity is ...

ZenteiQ uses GKE Inference Gateway and llm-d to boost multi-model inference throughput 4x and keep foundation model ...

Our NER pipeline, despite its simplicity in setup, did a pretty good job.

Choosing between custom and off-the-shelf LLMs affects cost, security, and control. This framework helps CTOs match the right approach to their actual use case.

Despite widespread adoption of large language models across enterprises, companies building LLM applications still lack the right tools to meet complex cognitive and infrastructure needs, often ...

Businesses actively deploy large language models (LLMs) by using popular AI models. Some companies do it to boost employee performance and enhance customer service. Others leverage LLMs for smart task ...