AI DATA CLOUDFUNDAMENTALS

Learn about some of the most relevant topics around cloud data warehousing, AI, data lakes, data engineering and other areas of interest related to cloud data analytics, AI and cloud data platforms.

Results

Newest - Oldest

The Value of Free and Public Data in the Modern Data Economy

As organizations seek to extract greater value from their analytics and AI initiatives, many are turning to external data sources to complement their internal data sets. Public, open data sets — those that don’t require special access requests or cost to access — are freely available and oftentimes critical inputs for organizations.

What is a Data Catalog?

Discover the significance of data catalogs, metadata management, and the benefits and key features that make them crucial for modern enterprises.

Data Streaming Essentials

Data streaming involves the continuous flow of data, facilitating real-time processing and analysis as information is generated. This real-time capability is crucial for applications requiring timely insights, such as fraud detection, recommendation systems and monitoring systems.

Extracting Insights from IoT Data

To maximize the value of Internet of Things (IoT) data, organizations need a cloud architecture and an effective analytics strategy.

What is Gradient Boosting?

Gradient boosting is a machine learning (ML) technique used for regression and classification tasks that can improve the predictive accuracy and speed of ML models.

Uniting Data Providers, Apps and Marketplaces for Business Intelligence

As organizations increasingly seek to enrich their internal insights with external intelligence, two critical components have emerged at the heart of the modern data economy: data suppliers and data marketplaces.

AI in Business and Product Development

From enhancing decision-making to powering adaptive products, AI is reshaping how companies operate, compete and deliver value.

What Are AI Agents? Understanding Their Role and Impact

AI agents are revolutionizing modern business by automating tasks and enhancing decision-making through advanced algorithms and machine learning techniques.

Building Effective Machine Learning Pipelines

To maximize the impact of ML models, organizations must adopt structured, scalable and automated ML pipelines – supported by efficient data deployment practices.

What Is Data Engineering?

Data engineering is the practice of designing and maintaining systems for collecting, storing and processing data to support analysis and decision-making.

A Modern Approach to the Operational Data Store

Learn how an operational data store works, the potential benefits of using one, and how it can give businesses access to the data they need more quickly and efficiently.

Data Analytics for Retailers

Retail customers expect shopping experiences to be personalized to their unique needs and preferences. Data analytics enables a data-driven approach to meet these expectations.

Previous

1

2

3

4

5

Next