๐ŸŽ‰ New! GradientPond now supports LLM evaluation and prompt tracking. Here's how โ†’

Integrations

GradientPond works with every major ML framework, cloud provider, and tool in your stack.

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PyTorch

Native callback integration. Auto-log training metrics, gradients, and model checkpoints.

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TensorFlow / Keras

Keras callback and TF summary writer integration for seamless experiment tracking.

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Hugging Face

Trainer integration for transformers. Track fine-tuning runs and model evaluations.

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AWS SageMaker

Deploy tracked models directly to SageMaker endpoints with full lineage preserved.

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Google Cloud AI

Vertex AI integration for training, serving, and monitoring models on GCP.

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Lightning AI

PyTorch Lightning logger for automatic metric and hyperparameter capture.

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LangChain

Trace LLM chains, track token usage, and evaluate chain outputs end-to-end.

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LlamaIndex

Monitor RAG pipelines, track retrieval quality, and evaluate generated responses.

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Docker & Kubernetes

Containerized deployments with Helm charts and K8s operator for on-premise installs.