Building and End-to-end MLOps Pipeline

Aurimas Griciūnas, a Senior Solutions Architect at Neptune Ai and the CEO of Swirl AI delivers a talk on MLOps (Machine Learning Operations) and the lifecycle of ML projects. He highlights the stages involved in the ML project lifecycle, including feedback loops, deployment, monitoring, and experimentation. Griciūnas explores training and inference pipelines, specifically batch pipelines and real-time inference pipelines. The talk emphasizes the significance of CI/CD (Continuous Integration/Continuous Deployment) and high ML maturity pipelines while acknowledging the challenges associated with their implementation.