What I like best about Iguazio is its strong focus on production-ready AI. It simplifies the ML lifecycle by integrating real-time data processing, feature engineering, and model deployment in one platform. The built-in feature store, serverless functions, and automated pipeline orchestration make deploying and managing models seamless. It’s especially useful for teams that need to move quickly from development to production without relying on multiple tools or manual steps. Iguazio is truly built for scalable, enterprise-grade AI.

The experience and interface is the same even if it is deployed in various compute environments, so you can combine different cloud or onprem resources in the same dashboard. The solution team was helping implementation very efficiently.

Iguazio brings super power in the hands of data scientists - with the push of a button, you can leverage your cloud compute for GPU accelerated model training and scale to zero for cost savings once completed. Using Kubeflow based pipelines, Iguazio enables machine learning training and deployment automation, leading to tremendous productivity improvement, and allowing an ML team to scale well beyond its normal capacity. The MLRun library further enhances the team's capabilities by implementing numerous common use cases, such as artifact logging, and experiment tracking. The Iguazio technical support team is second to none - using their deep expertise in Kubernetes, DevOps, machine learning and sofware development, they provide responsive support on a comprehensive list of topics.