AWS machine learning
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.
AWS Machine learning Offerings
Get the support and expertise you need, across the full AWS ML project lifecycle:
We develop solutions for your businesses toughest data problems. Assess, model, train, and optimize to give you confidence in each solution.
Machine Learning Engineering
Our team of multidisciplinary ML engineers and architects helps you harden, scale, and integrate ML applications to deliver real value — defining MLOps processes and infrastructure patterns for repeatable future success.
Managed Machine Learning
We deploy and manage your ML models, with 24x7 monitoring and alerting, as well as proactive remediation (such as model refitting) to rectify problems before they impact the business.
Making machine learning work on AWS
Machine learning that
Our multidisciplinary ML teams bring the combination of data science expertise, engineering discipline, and AWS know-how you need to build, optimize, and scale production-ready ML applications that integrate with key business systems.
Proven paths to accelerate
We get your models into production. By leveraging tested deployment patterns, implementation frameworks, and automation, we do it faster and with less risk — all while freeing data scientists to focus on solving business problems.
Putting your data to work in
From use-case exploration to data identification and acquisition, we help you identify ML opportunities, obstacles, and goals from data discovery to model training, we provide the engineering perspective to help ensure a measurable and successful delivery.
Accuracy and operational
Drifting models yield erroneous predictions, which lead to financial damage and risk. We help ensure models are appropriately tested and validated; from there, we work to detect issues, ensure visibility, and proactively identify and refit drifting models before they harm the business.
Case Study: Top-5 U.S. Restaurant Chain
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.
Why phData for Machine Learning on AWS?
Data science smarts, engineering know-how, and
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.
Support and expertise across the full ML lifecycle
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.
A vigilant, systematic approach to ML security
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.