Building a machine learning application and deploying into production usually takes a large Data Science team at least a year.
It requires choosing dozens of storage and middleware technology components and challenges can include:
- 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, auding, and provenance
In this webinar, we show how to build an entire machine learning application from scratch that can go into production in 3 weeks and meet the stringent response time and availability of SLAs at extreme scale.