Organizations everywhere are automating the development, deployment, monitoring, and governance of mission-critical machine learning (ML) and other artificial intelligence (AI) applications. Operational data science is a collaborative process that increasingly goes under the name of MLOps. Organizations are bringing the latest MLOps into their data science workflows to augment the productivity of data engineers, statistical modelers, and other highly skilled personnel. Mature enterprise MLOps processes leverage cloud-native infrastructure to scale the deployment, monitoring, and management of statistical models and code builds into production applications. Join Dan Darnell from Dataiku and TDWI’s senior research director James Kobielus for this webinar to learn how enterprises can succeed in using mature MLOps practices across their entire data science pipelines to speed deployment of their most sophisticated AI applications. Key topics that he will discuss include: - Business opportunities that are driving demand for MLOps - Key investments in data ingestion, cleansing, preparation, and modeling technologies that are essential for organizations to succeed with MLOps - Challenges that organizations face when implementing MLOps within their established data science processes - Principal operational metrics that organizations must monitor and track to ensure the success of their MLOps initiatives while mitigating associated operational, legal, and regulatory risks