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Leveraging AMPs for machine learning

CIO

Data scientists and AI engineers have so many variables to consider across the machine learning (ML) lifecycle to prevent models from degrading over time. Explainability is also still a serious issue in AI, and companies are overwhelmed by the volume and variety of data they must manage.

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How AI can alleviate help desk workloads

CIO

We end up in a cycle of constantly looking back at incomplete or poorly documented trouble tickets to find a solution.” The number one help desk data issue is, without question, poorly documented resolutions,” says Taylor. High quality documentation results in high quality data, which both human and artificial intelligence can exploit.”

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Introducing Accelerator for Machine Learning (ML) Projects: Summarization with Gemini from Vertex AI

Cloudera

Were thrilled to announce the release of a new Cloudera Accelerator for Machine Learning (ML) Projects (AMP): Summarization with Gemini from Vertex AI . We built this AMP for two reasons: To add an AI application prototype to our AMP catalog that can handle both full document summarization and raw text block summarization.

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5 Benefits intelligent document processing brings to content management

CIO

As explained in a previous post , with the advent of AI-based tools and intelligent document processing (IDP) systems, ECM tools can now go further by automating many processes that were once completely manual. That relieves users from having to fill out such fields themselves to classify documents, which they often don’t do well, if at all.

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Data Science Fails: Building AI You Can Trust

The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO

Before LLMs and diffusion models, organizations had to invest a significant amount of time, effort, and resources into developing custom machine-learning models to solve difficult problems. In many cases, this eliminates the need for specialized teams, extensive data labeling, and complex machine-learning pipelines.

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The dawn of agentic AI: Are we ready for autonomous technology?

CIO

Ive spent more than 25 years working with machine learning and automation technology, and agentic AI is clearly a difficult problem to solve. Document verification, for instance, might seem straightforward, but it involves multiple steps, including image capture and data collection, behind the scenes.