Machine Learning

Machine learning opens the door to the sort of predictive intelligence and innovative automation capabilities businesses need to stay competitive.

End-to-end solutions to put models into production.

Machine Learning gets your models into production. With services across the full ML lifecycle, our experienced data scientists and engineers help you build, train, and deploy ML models, then ensure those models continue delivering value — without requiring you to build your own team of expensive ML specialists. And with our enterprise-tested MLOps Framework, you’ll have a repeatable process to systematically train, deploy, and monitor systems of models, track experiments, and scale your innovations.

Machine Learning Process

Machine Learning Services

Data Science

We provide the resources and expertise you need to handle use case discovery, feature engineering, model training, validation, and everything in between.

ML Engineering

We bring the expertise you need to scale and harden trained models, then integrate them with business systems and pipelines — building with an automated, cloud-centric approach to deploy faster, more reliably, and at lower costs.

Managed ML

We offer architecture and 24/7/365 proactive monitoring and alerting of model performance, data quality, and application/platform health — taking action to refit models and remediate issues before they adversely impact the business.

Why phData Machine Learning?

Data scientists who get engineering, engineers who get data

At phData, engineering is our DNA. 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. And as a pioneer in MLOps, we’re one of the few providers that has actually built, deployed, and successfully supported MLOps architectures.

End-to-end machine learning expertise

We bring the right people with the right skills at each stage of the full machine learning lifecycle — from ideation to model training to deployment to post-implementation support. 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

With experience implementing successful machine learning projects in highly regulated industries, we know how to navigate strict security and compliance regulations and to help our customers avoid common pitfalls. Our emphasis on automation, repeatability, data transparency, and process standardization minimizes human error — and therefore minimizes risk.

Our MLOps Framework

We provide an enterprise-tested MLOps 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.

Case Study: Restaurant Chain

A top-5 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.

Making Machine Learning work at scale

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.

Machine Learning that delivers

With phData, you get both the data science and engineering support you need — at every stage of the machine learning lifecycle.

Models that will see the light of day

Make sure your models actually make it into production. We bring the right mix of data science and engineering know-how to write quality code and package projects cleanly for handoff to engineering — minimizing the risk of project failure, IT roadblocks, and model performance issues.

Accuracy and operational confidence

Our data scientists ensure models are properly validated and tested. And once they're in production, our team works around the clock to detect issues, to identify drifting models, and to proactively refit them before the business is impacted to ensure they continue to provide accurate predictions.

Enterprise-grade applications build trust

Without scaling and hardening ML code with things like error handling, logging, and tests, ML organizations will struggle moving to production and gaining IT trust. Our team of professionals eliminate this concern by building robust and scalable ML systems.

A framework for scalable intelligence

Track and iterate ML experiments at scale. Our standardized, enterprise-proven MLOps Framework institutes an opinionated and automated “assembly line”-style workflow for getting models trained, deployed, updated, and secured — and doing it all faster, more efficiently, and with less risk.

Take the next step
with phData.

Learn how phData can help solve your most challenging data analytics and machine learning problems.

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