Data Engineering
for MLOps

Optimize price/performance to continuously improve the yield of your ML model training jobs.
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.


GitOps 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.

CloudGeometry GitOps Lifecycle for Data engineering
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.
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