In this final installment of our 4-part blog series on deep learning and artificial intelligence revolution, we explored why MongoDB is being used for deep learning. MongoDB's flexible data model makes it easy to store and combine data of any structure without sacrificing sophisticated validation rules to govern data quality. The schema can be dynamically modified without application or database downtime. This flexibility is especially valuable in deep learning, where constant experimentation to uncover new insights and predictions is required. MongoDB offers a rich programming and query model, including native drivers and certified connectors for developers and data scientists building deep learning models with data from MongoDB. The PyMongo driver is the recommended way to work with MongoDB from Python, implementing an idiomatic API that makes development natural for Python programmers. The database provides strong consistency by default, enabling deep learning applications to immediately read what has been written to the database, thus avoiding developer complexity imposed by eventually consistent systems. MongoDB is serving as the database for many AI and deep learning platforms, including IBM Watson, x.ai, Auto Trader, Mintigo, and Geo-Location Analysis for Retail, due to its performance, scalability, redundancy, and tunable consistency features.