Machine Learning Engineering

Machine learning requires technologies, dependencies, and artifacts that can be unfamiliar to traditional software and data engineering teams. Operationalizing machine learning and building repeatable processes requires development of complex software applications and pipelines for both models and data. With specialized skills and training in machine learning engineering on AWS, phData can help.

• Deploy machine learning models into production using best practices for version control, lineage tracking, and proper governance
• Implement an MLOps framework and automation which enables repeatable, reliable development and deployment of future models
• Develop systems for monitoring deployments and proactively raise alerts for potential issues before they become a problem
• Preserve the accuracy of your models by detecting data drift and automatically retraining using the latest data

Without effective MLOps practices, many machine learning models never see production usage and die in PowerPoint presentations. Successful machine learning projects require knowledge of best practices and experience with common pitfalls to scale up from experimental POCs to critical business applications.

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.