Operationalize Machine Learning at Scale
ModelOps, a derivative of DataOps, frees teams from the daily frustrations of operationalizing ML and AI models and empowers them to deliver continuous business value. The DataKitchen DataOps Platform simplifies the process by orchestrating your end-to-end ML pipelines for seamless collaboration, training, deployment, monitoring, and governance.
Safely Develop and Train New Models
With the DataKitchen Platform, quickly spinup aligned, fit-for-purpose ‘Kitchens’ where Data Scientists can experiment, test, and train models, safely and independently.
Say goodbye to the days when ML models wither on the vine waiting to be deployed to production. When new models are ready, Kitchens streamline deployments to different release environments by remapping to toolchain connections – wherever they are located (cloud, hybrid, on-prem). The DataKitchen Platform automates manual deployment steps so that your process evolves from patchwork to a true continuous deployment pipeline.
Monitor Model Performance
DataKitchen’s Platform enables you to continuously test and monitor your models in production. Customize alerts so your teams are instantly notified when models drift or underperform so you can get a head start on retraining.
Securely govern machine learning operations. The DataKitchen Platform provides control over who can run or update a model, as well as unprecedented visibility into usage metadata, lineage, and order run history. When a rule is violated, send alerts or automatically revoke access.
Orchestrate Across Your Entire Data Ecosystem
Your model is not an island. Data Science requires a high level of technical collaboration with other parts of the data organization. The DataKitchen Platform orchestrates your entire pipeline – from data access to value delivery – for seamless integration of all the heterogeneous data centers, tools, infrastructure, and workflows required for successful model development and deployment. The Platform’s single view also provides end-to-end visibility into the entire pipeline so teams can collaborate effectively.
A Great Model is Not Enough: Deploying AI Without Technical Debt
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