AI/ML maturity and assessment
To assess the level of an organization’s readiness to adopt ML at different stages, the concept of ML maturity is often used as a measure. ML maturity refers to the organization’s capability to implement ML successfully from multiple dimensions. At a high level, there are four key dimensions that can be considered when describing an organization’s ML maturity:
- Technical maturity: This refers to the technical expertise and capabilities of the organization in the domain of ML. Technical maturity can be measured in terms of the sophistication of ML algorithms and models used, the quality and availability of data, the scale and efficiency of ML infrastructure, and the ability of the organization to integrate ML with other systems and processes.
- Business maturity: This refers to the extent to which ML is integrated into the organization’s product development lifecycle, business processes, and decision making. Business...
