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.
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.
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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.
Feature Engineering vs. Feature Stores
Understanding the relationship between feature engineering and feature stores is vital for developing strong machine learning models.
Understanding extract, load, transform (ELT)
Extract, load, transform (ELT) has emerged as a modern data integration technique that enables businesses to efficiently process and analyze vast amounts of information.
Bridging SQL Development and Event-Driven Architecture
Despite their separate roles, SQL development and event-driven architecture (EDA) are essential components for building scalable, real-time and collaborative data systems.
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