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    Machine Learning and AI Deployment for IT and DevOps

    It’s what we do.

    Deploy any ML or AI Model as a Microservice with FastScore.

    Based on Containers

    Based on Containers, FastScore Deploys and Machine Learning or AI Model

    Watch this video showing simultaneous Python and R deployment. 

    Telemetry

    Model Telemetry

    Check out this video that explains how to monitor mission critical ML and AI workflows.

     

    Getting Started

    Getting Started with FastScore

    Jump to our Getting Started Guide, and learn how to quickly deploy models as a microservice.

    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:

    • Data science and IT need to align on hand-off processes for models
    • IT should implement uniform, scalable processes for model operations
    • Teams should leverage standard techniques like code repos,  CI/CD workflows, and automation
    • Production systems seamlessly support open source as well as classic data science methods

    Check out this webinar which outlines the details of how that alignment starts, by adopting an Model Development Life Cycle.

     

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    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:

    • Out of the box support for all major cloud providers and data science languages including open source like Python, R and Tensorflow
    • An open API to ensure competitiveness
    • An SDK so customers can build capabilities to their own needs
    • A CLI, so DevOps teams can operate ML and AI models with existing scripting techniques and CI/CD workflows

    Check out this video on orchestration, which is one example of how FastScore integrates the standard IT toolbox.

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    Architecture

    FastScore is designed to enforce key abstractions, which enable long term, uniform deployment success for the enterprise.  

    • Models should follow a uniform deployment process regardless of source
    • Data schemas should be defined by data science teams and supported by data engineering
    • Data streams enable a model to effortlessly move from batch to on-demand applications.
    • As is often the case, base architectures are critical to ensure both flexibility and scalability for the IT teams.

    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.

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