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How to Install Docker and FastScore in a Blank System [VIDEO]

Blair Fleming June 16, 2017
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Install Docker and FastScore in 5 minutes!

Want to install FastScore but not sure how to get it up and running?  Watch this 5 minute instructional video with Rehgan Avon, Product Manager walk through how to install both Docker and FastScore into your blank system.  The video will lead you through what prerequisites you will need, as well as how to configure the FastScore fleet, and more.

  • How to install python and set-up tools
  • Installing Docker and FastScore CLI
  • Launch model manage and install the FastScore Fleet

Docker Containers allow for easy install and set-up of FastScore.  Once installed you can view the dashboard and start scoring models in minutes. 

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Simultaneous Python and R Deployment in FastScore [VIDEO]

Garrett Long June 7, 2017
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run any model anytime regardless of its native data science language

Did you know FastScore, our agnostic analytic deployment engine, can run any model, any time, regardless of its native data science language?  Watch this 4 minute video, and see a gradient boosting machine model built in python, and the same model built in R, deployed to an AWS instance with three easy steps.  With the right abstractions, and leveraging microservices, you can easily deploy a model simply by:

  1. Loading models in any language into the scoring engine.
  2. Selecting an input stream that delivers data into the model.
  3. Selecting an output stream for where the data goes after scoring has been completed

Supported through both the FastScore dashboard and the command line, you are now able to load and started scoring models in minutes.

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Analytic Deployment via FastScore Enables an Analytic Operations Center

Garrett Long May 5, 2017
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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.

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