Explore how to bridge the gap between data scientists and production environments using the Tempo Python SDK in this 32-minute conference talk from MLOps World: Machine Learning in Production. Learn how data scientists can prepare, test, and deploy machine learning models efficiently, either directly or through CI/CD and GitOps processes. Discover techniques for integrating inference logic, outlier detectors, and model explainers into the workflow. Follow along as the speaker demonstrates deploying various models, from simple to advanced, including outlier detection, model explanation, and multi-model ensembles with custom business logic. Gain insights into local testing and production deployment on Kubernetes with Seldon or Kubeflow's KFServing. Understand how to accelerate the transition of machine learning models from research to production while ensuring proper implementation and monitoring.