Accelerating Deployment of AI with MLOps Orchestration

Explore the concept of MLOps Orchestration and learn how to build an MLOps stack over Kubernetes with Kubeflow, MLRun, and Nuclio in this comprehensive conference talk. Discover how to simplify the process of getting data science to production in any environment, from data collection and preparation to automated model training, deployment, and monitoring. Learn to drastically reduce time and effort needed for production-ready data science projects. Map business problems into automated ML production pipelines, identify appropriate tools, and run AI models at scale to accelerate business value. Gain insights from live demonstrations and real customer case studies across various use cases, including fraud prevention, real-time recommendation engines, and NLP. Understand how to leverage Kubernetes and open-source technologies to create continuous development and delivery (CI/CD) of data and ML-intensive applications while making deployment more efficient and scalable for a growing set of complex use cases.

Accelerating Deployment of AI with MLOps Orchestration