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Gantry

MLOps Platform

The continuous improvement platform for machine learning. Monitor model performance, detect drift, and close the feedback loop between production and training.

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About This Tool

Gantry is an MLOps platform that helps machine learning teams operationalize their models. It acts as the bridge between model training and production deployment. By tracking inputs, outputs, and user feedback in real-time, Gantry allows engineers to understand *why* a model is underperforming and curate the best data to retrain it.

How to Use

  1. 1. Install the Gantry SDK in your Python application.
  2. 2. Log model inputs and predictions to Gantry with one line of code.
  3. 3. Set up alerts for data drift or performance degradation.
  4. 4. Use the dashboard to analyze “bad” predictions and identify edge cases.
  5. 5. Export curated datasets to retrain and improve your model.

Key Features

📉 Drift Detection
🔄 Active Learning
📊 Evaluation Store

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Use Cases

Gantry is primarily used by ML engineers and data scientists in companies that have models in production. Common use cases include monitoring search recommendation engines, fraud detection systems, and increasingly, Large Language Model (LLM) applications like chatbots.

LLM Observability

With the rise of ChatGPT and custom LLMs, Gantry has pivoted to offer specialized tools for monitoring text generation. It can track metrics like token usage, latency, and “hallucination rates” by allowing users to give thumbs up/down feedback on chatbot responses.

Evaluation Store

Gantry introduces the concept of an “Evaluation Store.” Instead of just tracking metrics, it stores the actual inputs and outputs alongside their performance scores. This allows teams to query their production data (e.g., “Show me all queries where the model failed”) to build better test sets.

Limitations

Gantry is a technical tool requiring code integration. It is not a “no-code” solution for business users. It assumes you already have a model running in production and are looking to optimize it, rather than building one from scratch.

Integration

It integrates seamlessly with popular ML frameworks like PyTorch, TensorFlow, and OpenAI’s API. It also connects with data warehouses like Snowflake and BigQuery, making it easy to pull production data back into your training environment.