Training PyTorch Models with MongoDB Data

MongoDB

MongoDB is a source-available cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas. MongoDB has become one of the most wanted databases in the world because it makes it easy for developers to store, manage, and retrieve data when creating applications with most programming languages. MongoDB is popular because it's easy to learn and get started. It's highly scalable (auto-sharding) and cost-effective, and it has a flexible data model.

PyTorch Models

PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing. It was originally developed by Meta AI and now part of the Linux Foundation umbrella. PyTorch is designed to provide good flexibility and high speeds for deep neural network implementation. PyTorch uses dynamic computation graphs which makes it different from other deep learning frameworks. It has become a popular choice for deep learning tasks such as computer vision, natural language processing, and speech recognition. PyTorch has gained popularity for its simplicity, ease of use, dynamic computational graph, efficient memory usage, flexibility, speed, native ONNX model exports, which can be used to speed up inference. It also shares many commands with numpy which reduces the barrier to learning it.
With the growing popularity of both MongoDB for storage and PyTorch models for AI deployments, it is unsurprising that many organizations are seeking to train PyTorch models using data in MongoDB. Kaspian offers native connectors for this operation. Just register your MongoDB datastore and link your model training job; Kaspian's autoscaling compute layer makes it easy to train and deploy PyTorch models using any data in your cloud with minimal setup or management.
Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.

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