IBM Journey to AI blog
MAY 9, 2023
Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process.
IBM Journey to AI blog
NOVEMBER 27, 2023
That’s why we continue expanding the IBM and AWS collaboration, providing clients flexibility to build and govern their AI projects using the watsonx AI and data platform with AI assistants on AWS. .”* However, to be successful they need the flexibility to run it on their existing cloud environments.
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Snorkel AI
JUNE 12, 2023
With Snorkel Flow, we’ve transformed labeling training sets from an ad hoc manual process into a programmatic one, accelerating time to value by 10-100x+ and leading to better model accuracies for our enterprise customers, including five of the top 10 US banks, government agencies, and more.
Snorkel AI
JUNE 12, 2023
With Snorkel Flow, we’ve transformed labeling training sets from an ad hoc manual process into a programmatic one, accelerating time to value by 10-100x+ and leading to better model accuracies for our enterprise customers, including five of the top 10 US banks, government agencies, and more.
IBM Journey to AI blog
OCTOBER 3, 2023
And this year, ESPN Fantasy Football is using AI models built with watsonx to provide 11 million fantasy managers with a data-rich, AI-infused experience that transcends traditional statistics. But numbers only tell half the story. For the past seven years, ESPN has worked closely with IBM to help tell the whole tale.
Unite.AI
DECEMBER 6, 2024
Postgres, with EDBs enhancements, provides the essential flexibility for multi-cloud and hybrid cloud environments, empowering AI-driven enterprises to manage their data with both flexibility and control. EDB Postgres AI brings cloud agility and observability to hybrid environments with sovereign control.
Marktechpost
OCTOBER 21, 2024
Organizations require models that are adaptable, secure, and capable of understanding domain-specific contexts while also maintaining compliance and privacy standards. Traditional AI models often struggle with delivering such tailored performance, requiring businesses to make a trade-off between customization and general applicability.
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