End-to-end solutions from experimentation to production
The platform's one-click deployment function encapsulates the complete MLOps process: after the model validation is passed, the system automatically generates a Docker-containerized prediction API and hosts it on the AWS elastic computing cluster. Users can obtain an exclusive HTTPS endpoint that supports real-time request response in JSON format. Compared to the 2-3 week engineering cycle required for enterprises to build their own modeling services, DataFawn shortens the process to 5 minutes with built-in traffic monitoring and automatic scaling mechanisms.
Actual deployment cases show that after a retailer integrated a promotional response prediction model into its CRM system, the API response latency was stabilized at less than 200ms, and the O&M cost of handling 100,000 requests per day was only 1/5 of the traditional solution. more critical is the iterative management of the model provided by the platform, which allows the system to keep the old version of the API online when uploading a new version of the dataset and seamlessly switch to the new version after the A/B test confirms the improvement of the effect. The system will keep the old API online when uploading a new version of dataset, and then switch seamlessly after the A/B test confirms the improvement of the effect, which completely solves the risk of model update.
This answer comes from the articleDataFawn: A Data Analytics Platform for Building Machine Learning Models Without Writing CodeThe
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