TALKING

With trust, Universal AI becomes not only possible but inevitable.

They will be aware of roles and responsibilities across design, performance and maintenance of ML models and will monitor updates and refreshes to manage model drift. They will have established performance and risk metrics to protect potential value, and they will be dedicated to the ongoing auditing of models to check for any deterioration in the quality of results.

Over time, the MLOps team can only prevail if due attention is paid to the diverse training needs of not only the team itself but also non-technical colleagues. Onesize-fits-all training is unlikely to guarantee sustainable success in AI, so time and resources must be applied to strategic upskilling. Any training plan must be based on skills gaps identified in people with specific job roles.

Not just possible... inevitable

While everything starts with value demonstration, the organisation must emphasise governance as the underpinning element of long-term success in AI. Sound governance does not need to unduly impact speed and agility, but without it, the enterprise may only be accelerating towards failure. Today’ s AI platforms can help team leaders navigate these waters. They are designed for the upskilling process – intuitive for the user but with guardrails to protect the organisation from risk. Equally however, they offer tools for experts who demand more flexibility.

With all levels of expertise covered and governance included as standard, the organisation’ s AI platform allows it to move beyond quick wins and take its place in the AI race. AI platforms allow creators and consumers to trace model outputs back to the logic that supplied them, which enhances trust, which lays the groundwork for future successes. And with trust, Universal AI becomes not only possible but inevitable. p

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