BAIR

FEBRUARY 17, 2024

Python code that calls an LLM), or should it be driven by an AI model (e.g. Likewise, in a compound system, where should a developer invest resources—for example, in a RAG pipeline, is it better to spend more FLOPS on the retriever or the LLM, or even to call an LLM multiple times? Operation: LLMOps and DataOps.

AI 145

AI AI DataOps Data Pipeline 145

Iguazio

JANUARY 16, 2024

Iguazio is an essential component in Sense’s MLOps and DataOps architecture, acting as the ML training and serving component of the pipeline. With Iguazio, Sense’s ML team members can pull data, analyze it, train and run experiments, making the process automated, scalable and cost-effective. Enabling quick experimentation.

ML 52

ML ML DataOps Data Scientist 52

Iguazio

JANUARY 16, 2024

Iguazio is an essential component in Sense’s MLOps and DataOps architecture, acting as the ML training and serving component of the pipeline. With Iguazio, Sense’s data professionals can pull data, analyze it, train and run experiments. With Iguazio, data scientists and ML engineers start having superpowers.”

ML 52

ML ML DataOps Data Scientist 52

BAIR

FEBRUARY 18, 2024

Python code that calls an LLM), or should it be driven by an AI model (e.g. Likewise, in a compound system, where should a developer invest resources—for example, in a RAG pipeline, is it better to spend more FLOPS on the retriever or the LLM, or even to call an LLM multiple times? Operation: LLMOps and DataOps.

AI 40

AI AI DataOps Data Pipeline 40