PACE - ML is Mphasis Framework and Methodology to automate multiple stages in the machine learning (ML) pipeline, accelerating the lifecycle of development, deployment, and productionizing of ML algorithms. It is a combination of Mphasis proprietary tools and methodologies along with best in-class third-party as well as open-source tools. The end-to-end framework uses workflows, collaboration platforms and monitoring tools to improves the efficiency and streamline the management of model selection, reproducibility, versioning, auditability, explainability, packaging, re-usability, validation, deployment & monitoring.
PACE-ML is built on MLOPs principles to facilitate a set of practices and activities which enable data scientists and IT operations to collaborate and manage production pipelines of ML applications and services. It enables organizations to improve the quality and reliability of the ML solutions in production and helps automate, scale, and monitor them.
Improves speed and time-to-market for products and services
Reduces the effort and time in building & deploying models
Increases users' confidence in the system
Automated model pipeline management reduces manual interventions, decreases time for deployment and enables continuous delivery
Tracks model, code and data changes and increases collaboration among teams
Allows users to identify biases or defects in the system so that they can be corrected. Improves scrutability as users can tell the system when it is wrong
Monitors to ensure no broken models exist in production and responds to performance issues faster
Reduces cost of development through automation & seamless integration
Reduces risks through model explainability and compliance
Scalability and Reliability through enhanced collaboration, monitoring and automated deployments