Aqueduct is a framework that enables the orchestration of machine learning (ML) and large language model (LLM) workloads across multiple cloud infrastructures. It simplifies the deployment process by allowing developers to manage ML pipelines using familiar Python code while integrating seamlessly with platforms like Kubernetes, Spark, and AWS Lambda. Aqueduct offers a unified platform to monitor the performance and execution of models in real-time.
Features
- Python-native API for defining and deploying ML tasks
- Seamless integration with cloud infrastructures like Kubernetes, AWS Lambda, and Spark
- Centralized visibility for monitoring code execution, data, and model performance
- Supports multi-cloud setups without the need to overhaul existing tools
- Real-time monitoring and troubleshooting for deployed models
Use Cases
- Deploying machine learning models across multiple cloud platforms
- Orchestrating complex ML workflows while managing data and resources efficiently
- Scaling machine learning applications for industries like healthcare, finance, and e-commerce
- Monitoring and optimizing model performance using built-in analytics
- Creating reproducible workflows that integrate with existing ML infrastructures
Summary
Aqueduct excels by simplifying ML workflow orchestration with a Python-native interface, integrating easily with cloud infrastructures, and providing robust monitoring features. This makes it an essential tool for teams aiming to streamline their ML operations across diverse environments.
