article thumbnail

Bringing an AI Product to Market

O'Reilly on Data

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 362
article thumbnail

Synthetic data generation: Building trust by ensuring privacy and quality

IBM Big Data Hub

For instance, if a business prioritizes accuracy in generating synthetic data, the resulting output may inadvertently include too many personally identifiable attributes, thereby increasing the company’s privacy risk exposure unknowingly.

Metrics 81
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

A Golden Era of HPC in Government Meets Accelerating Demands

CIO Business Intelligence

In addition to quantitative ROI metrics, HPC research was also shown to save lives, lead to important public/private partnerships, and spur innovations. . Real-time big data analytics, deep learning, and modeling and simulation are newer uses of HPC that governments are embracing for a variety of applications. Government.

article thumbnail

Digital Twin Use Races Ahead at McLaren Group

CIO Business Intelligence

Aside from monitoring components over time, sensors also capture aerodynamics, tire pressure, handling in different types of terrain, and many other metrics. In the McLaren factory, the sensor data is streamed to digital twins of the engine and different car components or features like aerodynamics at 100,000 data points per second ?

article thumbnail

What you need to know about product management for AI

O'Reilly on Data

Otherwise, you will burn money paying external services for labeled data, and that up-front cost–before you can do your first demo–can easily be the most expensive part of the project. Without large amounts of good raw and labeled training data, solving most AI problems is not possible. Is the product something that customers need?

article thumbnail

AI Adoption in the Enterprise 2021

O'Reilly on Data

The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Bad data yields bad results at scale. Techniques.

article thumbnail

How Computer Vision is Revolutionizing the Manufacturing Supply Chain

CIO Business Intelligence

For personnel, cameras look for personal protective equipment (PPE) use, such as hard hats and safety glasses, and then the system either sends alerts to a manager if PPE is not being worn or keeps track of metrics that a safety officer uses to determine whether training is needed. How can we impact manufacturing revenue? .