Building a machine learning application to predict money laundering and deploying into production usually takes a large team at least at least a year.

It requires choosing dozens of storage and middleware technology components.

• Data volumes are high, and the application must scale accordingly while meeting stringent response time and availability SLAs.
• Stitching the dozens of technologies together requires a large number of subject matter experts.

It also requires the complex designing and testing pipelines for feature engineering, training, and inferencing. These pipelines are usually tightly coupled to the dozens of storage and middleware components, resulting in rigid and brittle systems. Then you have to build the database application, complete with dashboard, admin, pipeline management, monitoring, and governance, including entitlements, auditing, and provenance.

In this webinar, we show how to build from scratch an entire machine learning application that can go into production in 3 weeks and meet the stringent response time and availability SLAs at extreme scale.