phdata MLOps framework

An opinionated, automated machine-learning assembly line.
phData MLOps provides an enterprise-tested framework and automated workflow to get your machine-learning (ML) 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 ML models, you empower your teams not only to build innovative new capabilities, but to deliver those innovations at-scale.
Machine Learning phData
1 .

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.

2 .

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.

3 .

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 performance.

4 .

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 ML work at-scale

With phData MLOps, our team of data scientists and machine learning engineers helps you build out a repeatable architecture with process and automation to train, deploy, and monitor systems of models; track experiments; and ultimately scale your innovations.
  • Simplify movement between training and production with automation
  • Ensure consistent packaging and development across projects
  • Continuously monitor and assess model quality
  • Trace models from production all the way back to the data used to train them
MLOps Framework

Case Study: Top-5 U.S. Restaurant Chain

A top U.S. restaurant chain, already doing $10+ billion in annual sales, knew that to sustain their aggressive growth trajectory, they needed to continue making good on their core brand promise: delivering exceptional quality and customer service at a competitive price. They weren’t historically a tech-focused company. But to ensure consistency across several hundreds of restaurant locations, they needed to become one.

Why phData for MLOps?

Data scientists who get engineering, engineers who get data

We hire data scientists with a practical understanding of how to build and deliver models that actually hold up in production, and will continue delivering value at scale.

End-to-end machine learning expertise

As a pioneer in MLOps, we’re one of the few providers that has actually built, deployed, and successfully supported MLOps architectures. And we don’t just build a PoC and walk away; we help ensure your machine learning projects actually deliver ongoing value.

A security-minded approach to operationalizing ML

We know what it takes to make ML work in highly regulated industries. Our focus on automation, repeatability, data transparency, and process minimizes human error, and therefore risk.

Ready to learn more about our MLOps Framework? Let's chat.