Making it to Production with Machine Learning

As enterprises “make their own AI”, a new set of challenges emerge. Maintaining reproducibility, traceability, and verifiability of machine learning models, as well as recording experiments, tracking insights, and reproducing results, are key. Collaboration between teams is also necessary as “model factories” are created for enterprise-wide model data science efforts. Additionally, monitoring of models ensures that drift or performance degradation is addressed with either retraining or model updates. Finally, data and model lineage in case of rollbacks or addressing regulatory compliance is necessary.

H2O MLOps delivers centralized catalog and management, deployment, monitoring, collaboration, and administration of machine learning models. In this webinar, we learn how H2O can assist with operationalizing, scaling and managing production deployments.