Simultaneous analytic iteration and deployment with FastScore.
In our first post in a series of video blogs, listen in as George from our engineering staff takes Brooke from our customer team through a demo of FastScore and creates an Analytic Operation Center. In the demo, you will see two gradient boosting machine models deployed and scored in real time. Both model instances are deployed in FastScore, then the two model inputs and outputs are combined in a dashboard using Grafana – and we can start to monitor the analytics scoring as well as some key performance metrics of the deployment. Watch as they discuss several interesting concepts including:
- How can you quickly change models in production from Python to R?
- What happens to the compute resources when I change model languages?
- How can I leverage more analytic engines to increase scoring rates?
- Are there differences in running models in Azure vs AWS?
Centralized deployment, iteration and monitoring of analytics enables an Analytic Operation Center for the business – a single place to understand, manage and extract value from the data science investment.
