Joint customers can now use pre-built H2O.ai applications within Snowflake to build AI models that anticipate customer churn and boost retention
H2O.ai, the AI Cloud leader, today announced the launch of its automated machine learning tool as a pre-built solution for the Telecom Data Cloud, launched by Snowflake, the Data Cloud company. The Telecom Data Cloud unites Snowflake’s platform, Snowflake- and partner-delivered solutions, and industry-specific datasets.
With the Telecom Data Cloud, Snowflake and its ecosystem of partners can help telecommunications service providers accelerate digital transformation for the Telecom industry, enable superior customer experiences, maximize operational efficiency and monetize new data services.
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“It will take an ecosystem of technologies to maximize the Telecom industry’s potential for innovation,” said Phil Kippen, Telecom Global Head of Industry, Telecom at Snowflake. “Each partner for Snowflake’s Telecom Data Cloud was chosen with the highest regard for that mission and H2O.ai is no exception.”
Within the Telecom Data Cloud, customers can access industry-specific solutions from Snowflake’s ecosystem of partners to leverage best practices, reduce time-to-value, and increase overall impact. H2O.ai breaks down barriers holding back the everyday use of powerful AI tools. A pre-built suite of applications for Snowflake’s Telecom Data Cloud uses machine learning to help businesses maximize customer profitability and minimize churn. Equally powerful in macro or micro analysis, H2O.ai’s application can segment and score an entire customer base or model daily granularity for each customer. Models are built and yield results in minutes with predictions available to applications built on top of Snowflake.
“True industry transformation requires a corporate culture shift where there are fewer singular heroes and more winning teams,” said Sri Ambati, CEO and founder of H2O.ai. “A key to more winning teams is what we make: powerful AI that unlocks collaboration, creativity and interdisciplinary understanding.”
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