Flipboard

NOVEMBER 20, 2024

By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. Knowledge base – You need a knowledge base created in Amazon Bedrock with ingested data and metadata. In her free time, she likes to go for long runs along the beach.

AWS 157

AWS Natural Language Processing Machine Learning Machine Learning 157

Iguazio

JUNE 9, 2025

The blog is based on the webinar Deploying Gen AI in Production with NVIDIA NIM & MLRun with Amit Bleiweiss, Senior Data Scientist at NVIDIA, and Yaron Haviv, co-founder and CTO and Guy Lecker, ML Engineering Team Lead at Iguazio (acquired by McKinsey). You can watch the entire webinar here.

AI 52

AI AI Data Preparation ML 52

ODSC - Open Data Science

MAY 10, 2023

Although tabular data are less commonly required to be labeled, his other points apply, as tabular data, more often than not, contains errors, is messy, and is restricted by volume. One might say that tabular data modeling is the original data-centric AI!

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

Pickl AI

NOVEMBER 28, 2024

Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data. Model evaluation and tuning involve several techniques to assess and optimise model accuracy and reliability.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Iguazio

MARCH 20, 2025

To see the complete conversation and dive into their insights, watch the webinar here. See the webinar for more Gartner trends. The Enterprise AI Factory The enterprise AI factory includes: Data management - Curating data, preparing it, ingestion, etc. Watch the webinar to see. Whats Next in 2025?

AI 59

AI AI Data Pipeline Data Scientist 59

Data Science Dojo

APRIL 3, 2025

You must ensure continuous governance and security of your AI models and systems to prevent bias, data leaks, or any unauthorized AI interactions. A well-structured data pipeline ensures that AI applications work with clean, reliable, and regulatory-compliant data.

AI 195

AI AI Data Pipeline Data Preparation 195