Deploy any ML or AI Model as a Microservice with FastScore.
Today’s IT teams face a new challenge: how to operationalize all the machine learning and AI workloads their company is building including the variety of open source options like R, Python and Tensorflow. It’s not an easy problem, but FastScore can help. Read on to find helpful technical documentation and examples of how FastScore uniformly turns every analytic into a microservice, and allows the IT team to enable, manage and scale those assets in modern hybrid infrastructure.
Aligned Processes with Data Science
It’s critical as the enterprise scales their analytics capability that the IT and the Data Science teams are aligned:
Check out this webinar which outlines the details of how that alignment starts, by adopting an Model Development Life Cycle.
Tools for IT Success
FastScore is designed for deep integration to existing IT architectures. It’s not a “rip out and replace” world. Instead, FastScore brings modern tooling to support an incremental adoption journey. These tools include:
Check out this video on orchestration, which is one example of how FastScore integrates the standard IT toolbox.
FastScore is designed to enforce key abstractions, which enable long term, uniform deployment success for the enterprise.
Check out the information architecture behind FastScore, and start understanding how the right combination of abstractions, microservices and process helps solve the ML and AI deployment puzzle.