As companies become more comfortable with machine learning, the demand for real-time models is increasing, particularly for applications like offering immediate credit lines based on user data. Real-time machine learning is challenging due to the need for rapid data retrieval and maintaining complex infrastructure, unlike batch processing which allows for more relaxed response times. IT managers must manage two parallel pipelines for batch and real-time processing, each requiring considerations like data ingestion, model deployment, and API scalability. To address these challenges, Datagran offers a platform that simplifies the deployment of machine learning models for both batch and real-time processing, allowing businesses to focus on their core operations without being bogged down by complex infrastructure demands. Datagran's solution involves storing models in a central repository and enabling easy deployment via APIs, providing an end-to-end managed service that alleviates the burden on data scientists and IT teams.