For organizations to advance their ML capabilities and enable agility, reproducibility, auditability, and maintainability of their ML models, it is becoming increasingly necessary to incorporate MLOps practices. Thinking in terms of MLOps maturity levels can help assess the current state of the practice and create a roadmap for getting to the next maturity level as dictated by the strategic objectives of the business.

Making use of data in the right way has become a business imperative for all organizations, large and small. Implementing the right data management practices will go a long way to ensure that the end users are confident in the quality, security, and accessibility of the data.

The demands of digital transformations require organizations to quickly build standalone solutions to address market demands. Modern approaches to integration encourage keeping the domain-specific applications separate, mapping between the domain vocabularies as needed, while using an integration layer to standardize information exchange.