DataOps Software for Data Science and AI Teams
End ML and AI Project Failure
Gone are the days when ML and AI projects withered on the vine, never seeing the light of day. The DataKitchen DataOps Observability and Automation software simplifies the operationalization of ML and AI models by orchestrating end-to-end pipelines for seamless development, training, deployment, and monitoring.
DataOps provides the continuous delivery equivalent for machine learning and enables teams to manage the complexities around continuous training, A/B testing, and deploying without downtime. Automating these processes frees up the team’s time to focus on developing new models and use cases.
VP, Data Management & Analytics, Global Software Company
Collaborate Across the 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 Automation software 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.
Safely Develop and Train New Models
With the DataKitchen Automation software, quickly spinup aligned, fit-for-purpose ‘Kitchens’ where Data Scientists can experiment, test, and train models, safely and independently.
Deploy Quickly and Efficiently
The DataKitchen Automation software stops manual deployment steps so that your process evolves from patchwork to a true continuous deployment pipeline. 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).
Monitor Model Performance
DataKitchen’s Observability and Automation software enable 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.
A Great Model is Not Enough: Deploying AI Without Technical Debt
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