phdata MLOps framework
What is MLOPS?
The phData MLOps Framework
phData MLOps provides an enterprise-tested framework and automated workflow to put your machine learning models into production faster, more efficiently, and with less risk. By replacing manual, ad-hoc processes with an automated, opinionated “assembly line” to train, deploy, monitor, and secure machine learning models, you empower your teams not only to build innovative new capabilities, but to deliver those innovations at-scale.
More value, faster
By transforming a highly manual process (with standardized, infrastructure automation and templatized workflows), teams simply take new training data, drop them into cloud storage, and run the pipeline to retrain and redeploy.
Lower costs, better ability to innovate
Standardized infrastructure minimizes overallocation and waste. Developers, freed from having to build and deploy each model by hand, can focus on solving new business problems.
Improved reliability and risk mitigation
More automation means less human error. Infrastructure-as-code, templatized workflows, version control, and code centralization all reduce the risk of errors that impact availability, security, and model performance.
Minimized technical debt
Centralized monitoring and alerting, combined with highly visible lineage for each model, drastically reduced technical debt and increased the quality of the solutions in production over time.
Making Machine Learning Work at Scale
Case Study: Top-5 U.S. Restaurant Chain
Why phData for MLOps?
Data scientists who get engineering, engineers who get data
End-to-end machine learning expertise
A security-minded approach to operationalizing machine learning
We know what it takes to make machine learning work in highly regulated industries. Our focus on automation, repeatability, data transparency, and process minimizes human error, and therefore risk.