The GigaOm Radar reports support leaders looking to evaluate technologies with an eye towards the future. In this year's Radar for MLOps report, GigaOm gave Iguazio top scores on multiple evaluation metrics, including Advanced Monitoring, Autoscaling & Retraining, CI/CD, and Deployment. Iguazio was therefore named a leader and also classified as an Outperformer for its rapid pace of innovation.
The goal of MLOps is to make ML a fully integrated part of business operations. Enterprise MLOps involves implementing machine learning applications in live business environments, to solve specific business problems, often across multiple teams. This requires a platform with enterprise-grade capabilities, not only for development, but also for deployment at scale and post-deployment monitoring and management of models in production, in live business environments. The GigaOm Radar for MLOps helps buyers become familiar with the growing range of MLOps solutions and vendor offerings.
Why GigaOm Named Iguazio an Outperforming Leader for 2022
Iguazio is honored to be named an Outperforming Leader in GigaOm’s latest MLOps report. This recognition highlights our rigorous production-first approach to MLOps and differentiated capabilities that address the entire AI/ML lifecycle.
We're proud to see that the Iguazio MLOps Platform has been recognized for its ability to help enterprises scale, automate and accelerate enterprise AI, with features such as:
- CI/CD for ML
- Real-time serving pipelines
- An integrated online and offline feature store
- Built-in monitoring and retraining
What the Iguazio MLOps Platform Brings to Enterprise AI
As organizations continue to adopt machine learning, technical teams are shifting their focus from isolated projects to building 'ML factories'. The Iguazio MLOps Platform helps enterprises transform AI projects into real world business outcomes by accelerating and scaling development, deployment and management of AI services. Iguazio offers ML teams a single platform where data scientists, data engineers and ML engineers collaborate to operationalize machine learning and rapidly deploy and manage operational ML pipelines in production.
The Iguazio platform comes with dynamic scaling capabilities, automated model monitoring and drift detection. It also integrates with most open source and enterprise AI tools, providing an open and managed environment which works on-premises or on any cloud. At the heart of the Platform is MLRun, Iguazio's open source MLOps orchestration framework. MLRun provides a modular strategy for building production pipelines, allowing teams to create continuous, automated, and scalable ML pipelines without refactoring code, adding glue logic, or spending significant efforts on data and ML engineering.
To find out more about the GigaOm Radar for MLOps Report and see the full list of vendors included, check out the full report here (for GigaOm subscribers).