Steps for vanquish the last mile of analytics
With continued investment in Data, analytics and AI, as well as the broader availability of machine learning tools and application, organization have an abundance of analytical assets. Yet the creation of analytical assets should not be the only measure of success for organizations. In reality, DEPLOYING, operationalizing, or putting analytical assets into production should be the driver for how organizations are able to get value from their AI and data science efforts.
In a traditional Data and analytics continuum, data is transformed into insight to support decision-making. If organizations want to break out from experimentation mode, avoid analytics assets becoming shelf ware, and empower front lines to make analytics-powered decisions, they must start with decisions. Then they need to decide how to find, integrate and deliver the insights; and identify data to enable that.
Understanding technology Components
The centralized model repository, life cycle templates and version control capabilities provide visibility into commercial and Open Source analytical models, ensuring complete traceability and governance.
The deployment step is all about integrating analytical models into a production environment and using it to make predictions. It is often the most cumbersome step for IT or DevOps teams to handle, but it’s essential in delivering value.
Once organizations start realizing the value from analytics, the real world does not stop. scores need to be analyzed and monitored for ongoing performance and regularly evaluate whether models are behaving as they should as market conditions and business requirements change and new data is added.
If model performance degrades, organization should take one of three approaches:
i. Retrain the existing model on new data.
ii. Revise the model with new technique (such as feature
engineering, new data elements, etc.).
iii. Replace the model entirely with a better model.