IBM Big Data Hub

APRIL 15, 2024

Or we create a data lake, which quickly degenerates to a data swamp. Additionally, these accelerators are pre-integrated with various cloud AI services and recommend the best LLM (large language model) for their domain. Contextual data understanding Data systems often cause major problems in manufacturing firms.

Manufacturing 102

Manufacturing Contextual Data Knowledge Discovery Data Lake 102

Cloudera

JANUARY 30, 2024

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.

Manufacturing 77

Manufacturing Contextual Data IoT Digital Transformation 77

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. After all, Alex may not be aware of all the data available to her.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

CIO Business Intelligence

NOVEMBER 4, 2022

We’ll work with those scientists and actually build the computer models and go run it, and it can be anything from sub-physical particle imaging to protein folding,” he says. “In In other cases, it’s more of a standard computational requirement and we help them provide the data in the right formats.

Data Lake 109

Data Lake IT Experimentation Data-driven 109

O'Reilly on Data

OCTOBER 19, 2021

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.

IT 346

IT Testing Experimentation Software 346

CIO Business Intelligence

APRIL 27, 2022

While a strategy and a roadmap are instrumental, they must be accompanied by a governance model led by a steering committee that champions the voice of the customer. Access governance models, monitoring, prevention and remediation strategies and identity risk scores (of internal and external users, including vendors) are top concerns.

Management 52

Management Risk Contextual Data Cost-Benefit 52