Aqueduct is an MLOps (Machine Learning Operations) framework that enables users to define and deploy machine learning workloads on any cloud infrastructure. It provides a comprehensive set of tools for managing the entire machine learning lifecycle, including data preparation, model training, deployment, and monitoring. Aqueduct also supports multiple programming languages and frameworks such as TensorFlow, PyTorch or Scikit-Learn.

The platform offers several key features like automatic resource allocation based on workload requirements; automated scaling up/down of resources based on demand; support for distributed computing with Kubernetes clusters; customizable workflows using YAML files; integration with popular CI/CD tools like Jenkins or GitLab.

Aqueduct's main goal is to simplify the process of deploying ML models in production environments by providing a unified interface across different cloud providers while reducing costs associated with manual configuration setup.

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