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Building ML Ops Organizations for Scale

Watch Pythian’s presentation at CDOIQ that discusses how you can deploy reliable models at scale.

Many organizations have now successfully created their first analytical model and leveraged it to drive new business value and more impactful decisions. Now the hard part begins, building an operational framework for the deployment of future models and management at scale with governed principles of performance, bias, responsiveness and accuracy.

Today’s Machine Learning Operations (ML Ops) capabilities are more than just technology. They are capabilities that must align with the model development process, a data engineering platform and an operational model.

Bringing together defined metrics, flexible technology stacks and a well-defined operational model will enable your organization to deploy reliable models at scale. The collection of these capabilities will allow the organization to move to higher levels of maturity in their operations, higher levels of reliability and enable scalability as the use of analytical models for decisioning increases.

To learn more about the approach that will allow you to scale sustainably - access our on-demand presentation.

Access this on-demand presentation.