Operational AI with Analytics Modernisation

<< Cross-post of the TW webinar by myself and my colleague Shraddha Surana. Thoughtworks Landing Page>>>>>


Looking at the latest trend, AIOps and MLOps platforms have grown into a huge enterprise marketplace, with vendors claiming that migrating to these new platforms will solve the modernisation goals and operational challenges.

The truth is to evaluate how these platforms fit to the enterprise context. What are your integration challenges with the existing infrastructure? What level of customisation is required to enable a production line with these news tools? Skill gaps in the org and up-skilling journey? Future product roadmap?

Despite this marketplace being surplus, enterprises are still trying to figure out the modernisation strategy. This is because every organization has a different current state, starting points, end points, budget constraints , people skills and product roadmap.

Highlights of this session:

a) 3 pillars of operational AI to “Democratize AI” within the enterprise, where data, models and AI services follow a self-service approach.

b) Develop “Streamlined  and Unified Governance” in your enterprise, where analytics and data science teams are still enabled for open innovation and not to operate with limited ecosystem

c) “Enterprise View” of all the analytical and data science assets for the businesses to collaborate and own innovations

d) Build the enterprise culture of “Proactive Decisions” , where business are enabled with data, model explainability and self-healing techniques

Webinar Recording:

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