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 and transparency across ML/AI teams
Iterative ML/AI task profiling and
Spark integration for iterative analytic processing


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.

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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Features & Benefits

For customers who prefer an open source approach, CloudGeometry features OptScale. It’s a single platform that provides configuration, automation, data collection, data verification, testing and debugging, resource management, model analysis, process and metadata management, serving infrastructure, and monitoring. Key benefits include:

Performance optimization

Integrates with ML/AI models, highlighting bottlenecks and providing clear performance and cost recommendations.


Specify a budget hyperparameters to run multiple experiments using various instance types to simplify experimentation & optimization.

Internal and external model-specific metrics

For your ML/AI experiments or production tasks, so data engineers and data scientists can collaborate on boosts to performance & cost optimization.

Cloud cost optimization

Vary price/performance via dynamic sizing, Spot/Reserved instances, Saving Plans, etc.
Also available as a managed services through our partnership with HyStax.
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