Remove Contextual Data Remove Data Lake Remove Data Science Remove Modeling
article thumbnail

Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

article thumbnail

The Award Winning Formula: How Cloudera Empowered OCBC With Trusted Data To Unlock Business Value from AI

Cloudera

Recently, Cloudera, alongside OCBC, were named winners in the“ Best Big Data and Analytics Infrastructure Implementation ” category at The Asian Banker’s Financial Technology Innovation Awards 2024. While these are great proof points to demonstrate how business value can be driven by AI/ML, this was only made possible with trusted data.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Achieving Trusted AI in Manufacturing

Cloudera

Here are some of the key use cases: Predictive maintenance: With time series data (sensor data) coming from the equipment, historical maintenance logs, and other contextual data, you can predict how the equipment will behave and when the equipment or a component will fail. Eliminate data silos.

article thumbnail

MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before. Compute.

IT 351