AI fuels the future; sophisticated machine learning models help organisations efficiently discover patterns, reveal anomalies, make predictions and decisions and generate insights. But the lack of consideration for the operational challenges needed to scale is significantly inhibiting the ability to deliver. Often, models are not even put into production.
As machine learning and AI increasingly become key drivers of organisational performance, enterprises are realising the need to shift from personal heroics to engineered performance to more efficiently move machine learning models from development through to production and management.
This session will address some of the crunchy questions you face when setting up an effective and efficient AI operating model supporting the full lifecycle from continuous ideation and prioritisation to development and operations.