The rapid growth of AI, driven by advancements in foundational models and generative AI, has significantly increased the demand for efficient data management and ingestion, particularly for large-scale datasets comprising images, videos, text, and other formats. Labelbox has responded to these needs by enhancing its data ingestion capabilities, allowing for 10x faster uploads and processing, which are crucial for evaluating model performance and continuous improvement. The platform supports versatile data ingestion methods, including direct string inputs and public or private URIs, making it adaptable to various data infrastructures. Best practices for handling large data volumes with Labelbox include using the Python SDK for programmatic uploads, chunking uploads to improve reliability and speed, and employing asynchronous processing to handle massive datasets efficiently. These improvements ensure that data is ingested quickly and reliably, enabling organizations to maximize the potential of their data and accelerate AI initiatives.