MLOps ensures your machine learning and AI pipelines are designed and run with robust, adaptable data streams. CloudGeometry integrates model training seamlessly with data streamed to and from existing business workloads. The goal: boost the ROI of selected algorithms by balancing model execution and resource consumption, delivering shorter innovation cycles and improved decision-making.
Metrics & transparency across ML/AI teams
Iterative ML/AI task profiling and modeling.
Data Lakehouse with Spark integration
Expert enablement for iterative analytic processing.
Cloud cost optimization
Vary price / performance via dynamic sizing, Spot / Reserved instances, Saving Plans, etc.
HOW IT WORKS
MLOps Lifecycle for Data Engineering
Build and run key foundation processes for the unique lifecycle requirements at the intersection of changing modeled data and changes to production software code.
Continuous Integration: Expands testing and validating code and components to testing and validating data, data schemas, and models.
Continuous Delivery: Integrates multiple software packages and services configured to align your ML training pipeline for feedback with model prediction and optimization.
Continuous Training: Does for ML systems what CI/CD does for applications, automatically retraining and serving models.
Enhanced pipeline: With automated data and model validation steps, including pipeline triggers and metadata management.
Pipeline Automation management: Featuring source control, test and build services, deployment services, model registry, features store, ML metadata store, and E2E pipeline orchestration.
Supported platforms
Our platform and cloud-agnostic approach applies systematic, closed-loop automation and monitoring at all steps of ML system construction, including integration, testing, release, deployment and infrastructure management.
