Machine Learning Pipeline Automation at Scale
A successful machine learning deployment requires far more than a good model. Data aggregation, feature cleaning, feature engineering, and model selection are all important aspects of the machine learning pipeline (read more here about Cortex machine learning pipelines). By focusing on consumer data machine learning, Cortex provides a variety of automated tools to make it easier to build high quality end-to-end machine learning pipelines. For instance, automated feature engineering tools translate raw behavioral events into thousands of features across dozens of temporal windows. These tools are proven to work at massive scale : billions of events and hundreds of millions of users.
Machine Learning Pipeline Integration
Building a high quality machine learning pipeline is only one step in driving business ROI. Cortex is focused on consumer data machine learning decisioning and has a variety of native integrations across data sources like CDPs, data warehouses in addition to integrations with CRMs, DSPs, and DMPs to activate the machine learning. In addition, Cortex provides native SDKs to activate machine learning directly in client experiences for next-best-action, next-best-offer, and dynamic decisioning (read more about the Decisioning SDK here). The SDK contains needed production features like explore-exploit, prescriptive & predictive modeling support, and decisioning weighting (more SDK features can be found here).
Leverage Real-Time Machine Learning
Cortex enables your team to combine both historical batch data and real-time data into a single decisioning framework which provides inference decisions in the 100ms range. This latency allows you to deploy machine learning experiences onsite in low-latency environments. In addition, the real-time framework is designed to enable your team to both contribute to which features will be engineered real-time and what types of batch historical features to leverage. Here’s an overview of the real-time machine learning framework.
Real-Time Machine Learning Decisioning for Customer Data
Deploy and monitor advanced models at massive scale. Leverage automated pipelines, tailored SDKs, and dozens of consumer technology integrations.
Power outbound marketing with advanced machine learning decisioning to maximize ad revenue and marketing ROI.