CIO Business Intelligence

AUGUST 21, 2024

The flashpoint moment is that rather than being based on rules, statistics, and thresholds, now these systems are being imbued with the power of deep learning and deep reinforcement learning brought about by neural networks,” Mattmann says. Adding smarter AI also adds risk, of course.

Risk 136

Risk Insurance Cost-Benefit Software 136

DataRobot Blog

MARCH 7, 2023

Organizations that want to prove the value of AI by developing, deploying, and managing machine learning models at scale can now do so quickly using the DataRobot AI Platform on Microsoft Azure. The capability to rapidly build an AI-powered organization with industry-specific solutions and expertise.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

Cloudera

FEBRUARY 16, 2022

Support for multiple sessions within a project allows data scientists, engineers and operations teams to work independently alongside each other on experimentation, pipeline development, deployment and monitoring activities in parallel. The AMPs framework also supports the promotion of models from the lab into production, a common MLOps task.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 364

Marketing Experimentation Metrics Testing 364

O'Reilly on Data

APRIL 19, 2021

We’ll look at this later, but being able to reproduce experimental results is critical to any science, and it’s a well-known problem in AI. First, 82% of the respondents are using supervised learning, and 67% are using deep learning. 58% claimed to be using unsupervised learning. Bottlenecks to AI adoption.

Enterprise 364

Enterprise Risk Modeling Manufacturing 364

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

CIO Business Intelligence

MAY 6, 2025

Governance challenges I commonly see: No consistent data definitions between departments No agreed-upon owner for key metrics like revenue or customer lifetime value Shadow IT spinning up their datasets and dashboards These gaps create delays, misalignment and risk. These capabilities are no longer theoretical or experimental.

Prescriptive Analytics 78

Prescriptive Analytics Predictive Analytics Descriptive Analytics ROI 78