Programmatically author, schedule, and monitor data workflows using Python-defined DAGs. Features modular executors, rich provider/operator ecosystem (Kubernetes, AWS, GCP), and built-in scheduling/monitoring for batch and ML pipelines.
ONNX (Open Neural Network Exchange) is an open ecosystem that provides an open source format for AI models, including deep learning and traditional ML. It defines an extensible computation graph model, built-in operators, and standard data types, focusing on inferencing capabilities. Widely supported across frameworks and hardware, it enables interoperability and accelerates AI innovation.
Orchestrates and scales Python-based AI/ML workloads from laptop to thousands of GPUs — exposing task and actor primitives plus high-level libraries for training, hyperparameter tuning, serving, RL, and data processing. Designed for heterogeneous accelerators and production ML pipelines.
Streamlit is an open-source app framework that turns Python scripts into shareable web apps in minutes. It enables data scientists and AI/ML engineers to build interactive data apps like dashboards, reports, or chat apps using pure Python, without front-end experience.
Simplifies training and scaling PyTorch models by providing a lightweight, opinionated wrapper for training loops, distributed strategies, and experiment orchestration. Modular components and integrations let you prototype on one GPU and scale to multi-node setups with minimal code changes.
