At Iguazio, we’ve spoken and written at length about the challenges of bringing data science to production. The complexity of operationalizing ML can generate huge costs in terms of work hours and compute resources, especially as successful projects get scaled up and expanded.
The ML lifecycle--including managing and transforming data, engineering real-time features, scaling up resources, monitoring models in production--contains a lot of heavy lifting. That’s why MLOps is a force multiplier, enabling even lean teams to quickly bring complex AI/ML projects to production.
We’re proud to share that the Iguazio Data Science Platform has been named a fast moving leader in the GigaOm Radar for MLOps report.
What does Iguazio bring to the MLOps Space in 2021?
Notably, the report notes the early emergence of feature stores in the MLOps space. The report noted Iguazio’s particular strength as a complete platform with a fully integrated feature store. The feature store, among other important functions, operates as a data transformation service, generating features for training, serving and monitoring in production, and is used to build real-time and offline features.
Iguazio also provides ML teams with a single platform for AI/ML and real-time analytics, enabling companies to operationalize machine learning and rapidly deploy operational ML pipelines to production. Dynamic scaling capabilities, automated model monitoring and drift detection and more are built in, all packaged in an open and managed platform.