Overall Satisfaction with Iguazio
It's a platform that enables you to develop and manage real-time AI applications at scale. A tool that provides data science, data engineering, and DevOps teams with one place to operationalize machine learning and rapidly deploy operational ML pipelines. It is fully integrated with automated model monitoring and drift detection capabilities.
- Dynamic scaling capacity.
- Central Metadata management.
- Data ingestion and preparation.
- No Cons for me. we are able to parallelize work within a single pod so that different workers can ingest data simultaneously.
- Handling all types of triggers.
- Model training and testing.
- Data connectors .
- Feature store for engineering, storing,analyzing and storing all available features.
- Enterprise management and support.
- Is a fully integrated solution with a user friendly portal.
- Offers great services for scalable data and feature engineering.
- Has real-time pipelines.
- Model tracking
Execution, experiment, data, model tracking, and automated deployment is done automatically through the MLRun serverless runtime engine. MLRun maintains a project hierarchy with strict membership and cross-team collaboration. End-to-end data governance is fully solidified and managed with authentication and identity management. Customers securely share data by providing access directly to it and not to copies.
Do you think Iguazio delivers good value for the price?
Are you happy with Iguazio's feature set?
Did Iguazio live up to sales and marketing promises?
Did implementation of Iguazio go as expected?
Would you buy Iguazio again?
It is built in a way that supports low latency real-time data processing. The model can be triggered using different streaming engines without the need to write additional codes. It has serverless that enables developers to write code [that] automatically transform to auto-scaling production workload, significantly reducing time to market and resources.