Generate Business Value with AI Using the Production-First Approach to MLOps
Automate and simplify the building, testing, deployment and monitoring of your ML pipelines and production data. Continuously train, test and deploy your ML models based on changes in your data and business requirements.
Integrate MLRun, Iguazio’s open source framework for orchestrating your ML pipelines, with CI engines to automatically run all ML operations. After each PR and code change, MLRun and the CI engine prepare the data, train models, test, deploy to clusters, monitor the models and send back feedback for retraining and deployment. CI/CD for ML simplifies, streamlines and accelerates the development and deployment process, ensures technological consistency and enables tracking and scalability.
Build and run complex workflows composed of local/library functions, external cloud services or other building blocks of your choice.
Track and version models, code, data, lineage parameters, artifacts and results with minimal effort.
Run ML pipelines with MLRun and KubeFlow, GitHub Actions, GitLab, Jenkins or other engines.
Elastically scale each step of the CI/CD pipeline. Specify the configurations, assign GPU, CPU and memory for each workflow step and MLRun will auto-scale.
“Using Iguazio, we are revolutionizing the way we use data, by unifying real-time and historic data from different sources and rapidly deploying and monitoring complex AI models to improve patient outcomes and the City of Health’s efficiency”
Nathalie Bloch, MD
Head of Big Data & AI at Sheba Medical Center’s ARC Innovation Complex
Learn how to implement the data mesh approach to data architecture with the Iguazio MLOps Platform