Throwing together ad-hoc machine learning models and proofs-of-concept is one thing, but delivering machine learning (ML) models at-scale — even on a platform like AWS — is another class of problem entirely.
Data scientists are primarily focused on optimizing models to better solve business problems, rather than ensuring those models will be performant and scalable for a given infrastructure use case; meanwhile, traditional software engineers may not be familiar with the unique combination of tools and languages that ML applications typically rely on.
AWS Machine Learning from phData gets your models into production. With leading experts on AWS ML and data technologies — and a proven track record delivering successful data products — we support you across the entire lifecycle of a machine learning project, from ideation to post-implementation support.
A leading U.S. restaurant chain ($10+ billion in annual sales) needed a more automated, streamlined ML process to scale up a computer vision application and help ensure quality control across a fast-growing number of locations.
phData provided the customer with an “assembly line” to get their ML models trained, deployed, updated, and secured faster, more efficiently, and with less risk — making the most out of their AWS investment and putting their ambitious goal of deploying 400+ forecasting models in 2020 well within reach.
At phData, engineering is in our DNA. We hire data scientists with hands-on experience building and delivering models that actually hold up in production on AWS.
With veteran Spark developers, deep AWS expertise (100+ certified developers), and a long resume of delivering successful data products, we know how to help you develop, train, experiment, and deploy models on AWS. Whether you need help with migrating your ML projects to AWS from on-prem infrastructure or alternative platforms, or with navigating the vast AWS service catalogue and understanding how you might incorporate ML tools like SageMaker, our experts are ready to help.
We provide the right combination of services, expertise, and technology to support the full machine learning lifecycle — helping you build repeatable processes and frameworks to iterate and deliver models faster.
From ideation to model training to deployment to post-implementation support, we help ensure ensure your AWS-based ML projects deliver value —whether it’s experiment tracking and model selection, or integrating platform-specific best practices for deploying models on AWS.
With experience implementing successful ML projects in highly regulated industries, we understand how to deliver ML models in the cloud while maintaining security and compliance standards. We help with model authorization, model cataloging, data-set and feature cataloging, model interpretability, audit, and monitoring to help ensure your ML projects adhere to appropriate legal, ethical, and regulatory constraints.
Also, our emphasis on automation, repeatability, data transparency, and process standardization minimizes human error — and therefore minimizes risk.