Forrester's latest report states a tool for model serving is a must-have capability to operationalize AI at scale in "Introducing ModelOps To Operationalize AI: The Core Capability That Enterprises Need To Deploy, Monitor, And Govern Machine Learning Models." 

Deploy your machine learning models in production with one command with Spell Model Serving with high performance and auto-scaling, deployable on the cloud with Kubernetes.

Documentation for Spell Model Serving can be found here

Streamline Machine Learning from Experimentation to Production

• A standardized end-to-end MLOps pipeline that tracks your ML projects from model experimentation to production deployment. 
• Intuitive web dashboard with all logs, request metrics and machine metrics. 
• Supports standard machine learning libraries like Tensorflow and Pytorch out of the box, easy to customize with additional dependencies.

Deploy to Production with One Command

• Kubernetes cluster management to save you time enabling model serving and autoscaling. 
• Easily deploy models that you've trained on Spell or uploaded to the platform. 
• Easy deployment with high performance load balancing and state of the art async web server. 

Model Management and Versioning

• Full transparency with end-to-end lineage tracking that shows where and how your model was trained. 
• An intuitive versioning feature that allows for faster experimentation of your models.
• Promote team collaboration by keeping model training details and notes all in one place. 

Schedule a personalized demonstration with a Spell representative to learn how Spell can help streamline and accelerate your machine learning development.