This video demonstrates how to build a real-time fraud detection model using agentic ML in Snowflake Cortex Code, where five natural language prompts can generate synthetic data, perform exploratory data analysis, train and evaluate a model, log it to a model registry, and run inference with latencies under 100 milliseconds, all on the same platform as the secured and governed data.
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Real-Time Fraud Detection with Agentic ML in Snowflake #machinelearning #automation追加:
Is moving data across platforms for real-time ML costing you time and money?
There's a better way.
Now you can run batch and online use cases on the same platform as your secured and governed data in Snowflake.
Build, deploy, and manage it all in one place.
Let me show you how easy it is to build and deploy a real-time fraud detection model with just five simple natural language prompts.
First, I'm going to [music] ask Cortex Code to generate synthetic data.
Perform exploratory data analysis.
And train a model and evaluate the performance.
And finally, log the model into model registry. [music] Now let's run some inference and [music] check the latencies.
And this all completed in less than 100 milliseconds.
That's how easy [music] it is to generate low latency predictions in real time on the same platform as your data in Snowflake.
>> [music]
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