Methodologies, automation, and expertise to rapidly drive exploratory data science into value-generating, ML systems.
Most companies have data scientists and can train models, but to get maximum value and get models into production, the entire data science lifecycle must be addressed. phData MLOps provides 24×7 intelligent monitoring and management of deployed machine learning infrastructure, applications, and models to ensure ongoing confidence in your ML System.
Driving Production ML Systems
Customers can train models, but they do not know how to do the Machine Learning Engineering to put them in production. That's where phData's DSE practice lives.
Data Science Enablement can be deployed to immediately help:
Make Data Scientists More Productive
- Quickly access and understand data.
- Reliable data pipelines create foundation for simpler model deployment.
- Utilize best-of-breed automation for fast results.
Compliment Existing Data Science Teams to Ensure Optimal Techniques
- Development, scale, and architecture coaching to validate success and business outcomes.
- Develop ML models with proper scale considerations, logging, error-handling, and operational transparency.
- Avoid the harsh pitfalls on your way to Data Science maturity.
Deploy Models to Production
- Move models out of the sandbox and into production.
- Gain feedback from the business regarding deployment decisions.
- Create sustainable systems for model retraining, redeployment, and improvement.
Ensure Ongoing Confidence in ML Systems
- Ongoing measurement and monitoring of model performance lowers risk and increases business confidence.
- Drive adoption through performance measurement.
“Comprehensive live monitoring of system behavior in real time combined with automated response is critical for long-term system reliability”
MLOps: phData Machine Learning Model Management
phData Machine Learning Model Management provides tooling, processes, and support to ensure ongoing confidence in your ML System.
- 24×7 ML System Monitoring and Alerting – proactive monitoring of system behavior in real time combined with automated response is critical for long-term system reliability.
- Secure tooling – ML system monitoring tools are fully integrated with enterprise authentication and authorization systems.