According to IDC research, the deployment and monitoring of models is now perceived as the most challenging aspect of model development in organizations surveyed. This should come as no surprise, as the research shows that, 77% of AI models remain in a preproduction state. As organisations have matured in their use of AI/ML, model development is closer to a “solved problem”, and now the governance and deployment of models have become the bottleneck. A structured Machine Learning Operations (MLOps) approach enables large organisations to achieve and sustain AI success and address these challenges. So, how can enterprises implement a robust MLOps framework? This is the question Nicholas dives deep into, during his session at the Data Innovation Summit 2023. Key takeaways:
- Business opportunities that are driving demand for MLOps.
- Challenges that businesses face when implementing MLOps within their established data science processes.
- Principal operational metrics that companies must monitor and track to establish the success of their MLOps initiatives while mitigating associated risks.
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