article thumbnail

The importance of data ingestion and integration for enterprise AI

IBM Journey to AI blog

In the generative AI or traditional AI development cycle, data ingestion serves as the entry point. Here, raw data that is tailored to a company’s requirements can be gathered, preprocessed, masked and transformed into a format suitable for LLMs or other models.

article thumbnail

Databricks + Snorkel Flow: integrated, streamlined AI development

Snorkel AI

In todays fast-paced AI landscape, seamless integration between data platforms and AI development tools is critical. At Snorkel, weve partnered with Databricks to create a powerful synergy between their data lakehouse and our Snorkel Flow AI data development platform. Sign up here!

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Unlock proprietary data with Snorkel Flow and Amazon SageMaker

Snorkel AI

The integration between the Snorkel Flow AI data development platform and AWS’s robust AI infrastructure empowers enterprises to streamline LLM evaluation and fine-tuning, transforming raw data into actionable insights and competitive advantages. Here’s what that looks like in practice.

article thumbnail

Databricks + Snorkel Flow: integrated, streamlined AI development

Snorkel AI

In todays fast-paced AI landscape, seamless integration between data platforms and AI development tools is critical. At Snorkel, weve partnered with Databricks to create a powerful synergy between their data lakehouse and our Snorkel Flow AI data development platform. Sign up here!

article thumbnail

Upstage AI Introduces Dataverse for Addressing Challenges in Data Processing for Large Language Models

Marktechpost

Addressing this challenge requires a solution that is scalable, versatile, and accessible to a wide range of users, from individual researchers to large teams working on the state-of-the-art side of AI development. Existing research emphasizes the significance of distributed processing and data quality control for enhancing LLMs.

article thumbnail

Generative AI operating models in enterprise organizations with Amazon Bedrock

AWS Machine Learning Blog

Decentralized model In a decentralized approach, generative AI development and deployment are initiated and managed by the individual LOBs themselves. LOBs have autonomy over their AI workflows, models, and data within their respective AWS accounts.

article thumbnail

Building Scalable AI Pipelines with MLOps: A Guide for Software Engineers

ODSC - Open Data Science

MLOps is the discipline that unites machine learning development with operational processes, ensuring that AI models are not only built effectively but also deployed and maintained in production environments with scalability in mind. Building Scalable Data Pipelines The foundation of any AI pipeline is the data it consumes.