Machine Learning for IT

As data scientists implement AI in the enterprise, it is crucial that they have the datasets, and the compute and storage resources available to accurately train and test machine learning (ML) models before they deploy these models in production environments. Data science is an iterative process that often requires dynamic allocation of IT resources in order to eventually create accurate ML models. The data science teams require help from their corporate IT in this process to allocate these resources either on-premises, in the cloud or a combination. In this webinar, we will walk through the following 3 areas that are important in this process and how H2O.ai makes this process easier for IT: - IT resource management for data science and machine learning workloads. - Provisioning of resources for machine learning workloads – training and validation phases. - Deployment of AI applications – in the cloud, on-premises or at the edge.