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Generative AI that’s tailored for your business needs with watsonx.ai

IBM Big Data Hub

Based on initial IBM Research evaluations and testing , across 11 different financial tasks, the results show that by training Granite-13B models with high-quality finance data, they are some of the top performing models on finance tasks, and have the potential to achieve either similar or even better performance than much larger models.

Testing 92
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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. The model outputs produced by the same code will vary with changes to things like the size of the training data (number of labeled examples), network training parameters, and training run time. Underneath this uncertainty lies further uncertainty in the development process itself.

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Machine Learning Product Management: Lessons Learned

Domino Data Lab

The last step for a PM is to “use derived data from the system to build new products” as this provides another way to ensure ROI across the business. Addressing the Uncertainty that ML Adds to Product Roadmaps. Here, Pete outlines common challenges and key questions for PMs to consider.

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11 dark secrets of data management

CIO Business Intelligence

Some of the paradoxes relate to the practical challenges of gathering and organizing so much data. Others are philosophical, testing our ability to reason about abstract qualities. And then there is the rise of privacy concerns around so much data being collected in the first place.

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Q&A with Chris Ortega: Dealing With Uncertainty Through Technology

Jet Global

To implement AI, you need four main resources: an algorithm, at least 15 years of data, massive amounts of data over that time period, and a way to test the algorithm and get feedback on its accuracy. It’s part of a mixed bag of tools that we use for data collection, tracking, reporting, and analysis.

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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature.

Metrics 156
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Leading infrastructure to accelerate electric power intelligence

CIO Business Intelligence

However, new energy is restricted by weather and climate, which means extreme weather conditions and unpredictable external environments bring an element of uncertainty to new energy sources. communication reliability, which supports minute-level data collection and second-level control for low-voltage transparency.