Retrospective: 6 Months Using MongoDB 7.0 for Our AI/ML Pipeline – 30% Faster Document Storage When we set out to modernize our AI/ML pipeline in Q4 2023, we needed a document store that could handle high-throughput training data ingestion, low-latency model artifact storage, and seamless integration with our existing Python-based ML stack. After evaluating Cassandra, PostgreSQL, and MongoDB 7.0, we chose MongoDB 7.0 for its native vector search support, flexible schema design, and proven scalability for unstructured ML workloads. Six months later, we’re sharing our results: a 30% improvement in document storage speed, reduced operational overhead, and key lessons for teams running similar workloads. Why MongoDB 7.0?…