Snorkel AI

JUNE 12, 2023

Developing this data for AI usage is often overlooked — but it is one of the most powerful ways to build an AI moat. If you are interested in accelerating the data backbone of your AI strategy with Snorkel’s Foundation Model Data Platform, please connect with our team here.

Data Platform 145

Data Platform Large Language Models Software Development ChatGPT 145

Snorkel AI

JUNE 12, 2023

Developing this data for AI usage is often overlooked — but it is one of the most powerful ways to build an AI moat. If you are interested in accelerating the data backbone of your AI strategy with Snorkel’s Foundation Model Data Platform, please connect with our team here.

Data Platform 141

Data Platform Large Language Models Software Development ChatGPT 141

AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

Machine Learning 131

Machine Learning DevOps Data Scientist Data Quality 131

IBM Journey to AI blog

OCTOBER 4, 2023

Also, a lakehouse can introduce definitional metadata to ensure clarity and consistency, which enables more trustworthy, governed data. All of this supports the use of AI. And AI, both supervised and unsupervised machine learning, is often the best or sometimes only way to unlock these new big data insights at scale.

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Metadata AI Strategy Data Scientist Big Data 238

AI News

SEPTEMBER 26, 2023

AI and machine learning (ML) models are incredibly effective at doing this but are complex to build and require data science expertise. With CustomerAI Predictions now generally available, Twilio Segment is putting the power of predictive AI at marketers’ fingertips. Here are four trends in AI personalisation.

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Big Data AI AI ETL 333

IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

Machine Learning 186

Machine Learning Metadata Automation AI 186

AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL.

Data Science 68

Data Science Data Scientist Data Ingestion DevOps 68