Developing efficient data-driven applications, especially using deep learning, requires access to large and diverse datasets. However, sharing and collecting sensitive data is extremely challenging due to privacy, ethical, and legal concerns. To address these challenges, we present TripleBlind, a practical privacy-preserving framework for creating and consuming data-driven applications from decentralized data and algorithms. TripleBlind provides a set of automated, high-level APIs that enable (1) extracting conclusions from remote data without moving it outside the owner's firewall, (2) training sophisticated AI models from decentralized data, and (3) consuming trained models for secure and efficient inference-as-a-service without compromising the privacy of either the model or the data. We focus in this tool demo on two tasks: First, we train a ResNet34 model using decentralized medical image data over the public Internet without "seeing" the raw data. Second, we utilize our secure multi-party computation protocol to run real-time inference using the trained model over the public Internet.