Blog archive, page 3

Generative AI

NVIDIA GTC 2024: We have the power, now we must tame it to build our AI-driven future

If there is one word to sum up this year’s NVIDIA GTC conference, it is “Power.” It's up to us to figure out how to use it.

By Kjell Carlsson2 min read

Two travelers using a tablet to help them with their trip while on a beach
Data Science Leaders

TripAdvisor's GenAI journey from billions of reviews to a virtual travel agent

See how TripAdvisor leverages GenAI to transform billions of reviews into a virtual travel agent tool.

By Yuval Zukerman5 min read

Vector databases
Generative AI

Domino accelerates GenAI with vector database access and RAG

Learn about Domino's new vector database access for enabling retrieval augmented generation (RAG) to ensure GenAI applications are reliable, accurate, and up-to-date.

By John Alexander6 min read

Life scientists working in the lab
AI Governance

Domino announces AI Gateway to streamline and govern access to large language models

Domino's new AI Gateway enables secure, governed, and seamless access to external Large Language Models (LLMs) for building Generative AI applications responsibly and cost-effectively,

By John Alexander5 min read

Data Science

Fitting gaussian process models in Python

A common applied statistics task involves building regression models to characterize non-linear relationships between variables. It is possible to fit such models by assuming a particular non-linear functional form, such as a sinusoidal, exponential, or polynomial function, to describe one variable's response to the variation in another. Unless this relationship is obvious from the outset, however, it involves possibly extensive model selection procedures to ensure the most appropriate model is retained. Alternatively, a non-parametric approach can be adopted by defining a set of knots across the variable space and use a spline or kernel regression to describe arbitrary non-linear relationships. However, knot layout procedures are somewhat ad hoc and can also involve variable selection. A third alternative is to adopt a Bayesian non-parametric strategy, and directly model the unknown underlying function. For this, we can employ Gaussian process models.

By Chris Fonnesbeck27 min read

Data Science Platform

Domino expands Generative AI capabilities with AI Gateway and Vector Data Access

Domino helps Enterprises Scale GenAI responsibly and cost-effectively

By Tim Law8 min read

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