If you are trying to scale AI/ML inside the enterprise, end-to-end MLOps solutions are not the answer

Fig 1: End-to-end MLOps platforms require dedicated, specialized resources for deployment, which means separate ML engineers to support each data platform even as they each have their own limitations about which data sources and data sinks they can support.

How can Wallaroo help?

Unlike end-to-end MLOps platforms, whether from the cloud providers (e.g., SageMaker from AWS, Vertex from GCP) or SaaS vendors (e.g., Databricks, DataRobot), at Wallaroo…

  • We are agnostic about where the models were trained or where they will be deployed
  • You can use the same SDK to deploy to a variety of environments
  • You get far better production inference performance than any other solution (up to 12x faster inference on 80% less compute)
  • We are hyper-focused on being as simple and powerful as possible
Fig 2: No matter where the model is built or the environment in which it will be deployed, Wallaroo provides enterprises with a single, standardized platform that fits the reality of the enterprise ecosystem



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Wallaroo enables data scientists and ML engineers to deploy enterprise-level AI into production simpler, faster, and with incredible efficiency.