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How Snowpark+Streamlit Easily Delivers Machine Learning Apps in Snowflake

Snowflake

A revolution in data analytics is coming soon. For more than 10 years, Snowflake has been dedicated to bringing the work—pipelines, machine learning (ML) models, and apps—to your governed data. Watch this on-demand Snow on Snow webinar. Why are we so sure? What’s next?

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Democratizing Data Through Search and Natural Language Processing in Cloudera Data Visualization

Cloudera

Since the release of Cloudera Data Visualization (DV) back in Oct 2020 , our primary mission has been to expand access to data analytics and predictive insights across enterprise businesses. Figure 1: Example menu-driven application within Cloudera Data Visualization, left panel menu allows navigation across multiple dashboards.

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4 Steps to Shopper 360 Success for Retailers and Consumer Goods Brands

Snowflake

Integrating shopper data into one single, queryable location creates a foundation for strategic decision-making, ensuring that every touchpoint is rooted in accurate, comprehensive data. Identity resolution is the intricate process of connecting multiple identifiers across various data touchpoints to single out one unique individual.

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CDP Data Visualization: Self-Service Data Visualization For The Full Data Lifecycle

Cloudera

From our release of advanced production machine learning features in Cloudera Machine Learning, to releasing CDP Data Engineering for accelerating data pipeline curation and automation; our mission has been to constantly innovate at the leading edge of enterprise data and analytics.

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AI-First Benefits: 5 Real-World Outcomes

Cloudera

The availability and maturity of automated data collection and analysis systems is making it possible for businesses to implement AI across their entire operations to boost efficiency and agility. In simple terms an AI process can out-perform a human at very specific tasks. Process acceleration. Faster decisions .

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Delivering Telecom Sustainability Targets Using Autonomous Networks

Snowflake

Autonomous networks use AI and machine learning (ML) to automate network management tasks, optimize resource allocation, and predict potential issues before they occur. Learn: This module is responsible for large-scale ML model training and a comprehensive model lifecycle management capability.

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Data Engineering Weekly #162

Data Engineering Weekly

Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects. Data engineers build the systems that store and process sensitive information.