phData Machine
Learning & MLOps​

We put models into production.

We provide services for the entire lifecycle of a machine
learning project, from ideation to post-implementation support.
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phData Machine Learning Services

Data science

phData’s Data Science Approach and
Capabilities Will Give You:

Use-Case Exploration

We help you scope your project to ensure a measurable and successful delivery.

Data Identification & Acquisition

Identify, ingest, and catalog all the data necessary to set data scientists up for success.

Data Exploration & Discovery

Determine if a model can be built and to prepare for training.

Feature Engineering

Drive maximum model performance with expertly crafted features.

Model Training

It takes experience to select the right model and an engineering mind to effectively train it. We select the right algorithm and know how to train the model at scale.

Validation & Testing

We ensure accuracy by establishing ongoing validation requirements to identify drift before it affects model performance.

Governance

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

Your data scientists are experts in data and algorithms, not tools and infrastructure. We specialize in machine learning tools and supporting data scientists.

Increased ML Platform Stability

Engineers and data scientists cannot be productive on a platform that is always down. Our team keeps systems running under the most grueling workloads.

Balancing Innovation and Security

Data scientists need access to an organization’s most valuable data, but also need to use tooling that is diffuse and unregulated. We secure the platform while allowing data scientists to remain productive so that customers’ CISO can sleep well at night.

Expertise Managing Risk Compliance

Most organizations have countless policies that keep them in bounds legally and ethically to protect their IP. Our team is experienced in navigating the intersections of corporate policy and open source software usage to keep customers protected.

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.

phData Machine Learning Engineer Data Engineer Data Scientist

Why phData Machine Learning Engineering?

Data Engineers and Data Scientists lack the skills and experience required to build, optimize, and scale machine learning applications that can integrate with business systems. Our machine learning engineers have a blend of skills from both disciplines that uniquely position them to fill this gap.
Real value from innovation comes through the ability to mass produce and distribute that innovation to consumers. This is no different with Machine Learning and AI. Our team of engineers has the recipes and best practices that customers need to start getting value from their data science initiatives.

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.

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MLOps Managed Services

MLOps Managed Service provides 24/7 monitoring and alerting that operationalized machine learning applications require and the corrective measures, such as model refitting, that are needed to rectify problems before they happen.

Operational confidence

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.

Accuracy, every time

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.

Proactive refitting

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.

Stability is our focus

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.

Ready to learn more phData Machine Learning & MLOps? Let's chat.