Learning & MLOps
We put models into production.
learning project, from ideation to post-implementation support.
Capabilities Will Give You:
Data Identification & Acquisition
Data Exploration & Discovery
Validation & Testing
We assist with model authorization, model cataloging, data set, and feature cataloging, model interpretability, audit, and monitoring to ensure ML and AI live within appropriate legal, ethical, and regulatory constraints.
Machine Learning Platform Managed Services
Keep data scientists productive with specialized support for machine learning tools and platforms. We manage machine learning tools, manage stability and multi-tenancy, deal with the different dependency stacks, and manage complex and unsupported open source tooling. With our experts on hand, data scientists are free to focus on solving business problems.
Specialized ML Platform Expertise
Increased ML Platform Stability
Balancing Innovation and Security
Expertise Managing Risk Compliance
Machine Learning Engineering
Harden, scale, and integrate machine learning to add real value. Our team of machine learning engineers and architects are experts at hardening and scaling machine learning applications and integrating them with business systems. ML engineers also define MLOps processes and infrastructure to help your data science organization scale.
Why phData Machine Learning Engineering?
It is a myth that data scientists have the ability to code their own applications. In reality, it takes engineering focus and discipline to produce machine learning applications that have proper error handling, logging, and can scale to meet production demand. This is where our machine learning engineers thrive.
Building one-off solutions is not the way to success in AI and Machine Learning. For an organization to be successful in data science, they must implement the proper infrastructure and processes that will allow them to move beyond implementing isolated POCs. We broadly call this MLOps and our engineers have deep experience in establishing these systems to enable scale.
MLOps Managed Services
Business and IT executives require faith in the solution being delivered. Our MLOps Managed Service gives observability and confidence to the black box that is the machine learning system.
Degraded models producing inaccurate predictions can cause a detrimental financial impact and regulatory harm to a company. Our team of operations engineers monitor the model once it's been put into production to ensure that it continues to predict accurately.
As a model’s performance drifts, customers need to be able to quickly address the problem before the business is impacted. As part of our managed service, we will automatically refit their model as it begins to degrade to ensure long-lasting success.
Many organizations struggle to keep machine learning applications stable because of the unique set of tools and languages (open source or proprietary) that are used to construct these solutions. Our team of experienced operators keep applications stable and delivering value.
THE ULTIMATE GUIDE TO BUILDING A MACHINE LEARNING SOLUTION
Did you know that more than 90% of the world’s data was generated in the last two years? That’s why machine learning models that find patterns in data and can make decisions are critical for businesses today.