Servo Labs

Servo Labs

Servo Labs is an open-source machine learning platform for deploying and managing machine learning models in production environments. It provides tools for model versioning, monitoring, explainability, and drift detection.

Servo Labs: Open-Source ML Platform

Deploy, manage & monitor machine learning models in production environments with Servo Labs, an open-source platform offering model versioning, monitoring, explainability & drift detection tools.

What is Servo Labs?

Servo Labs is an open-source machine learning platform designed to help data scientists and engineers more easily deploy, monitor, and manage machine learning models in production. Some key capabilities and benefits of Servo Labs include:

  • Model Registry & Versioning - Track different versions of models, compare performance metrics, and seamlessly rollout model updates.
  • Monitoring & Alerting - Monitor model performance metrics like accuracy and data drift over time. Configure alerts when thresholds are exceeded.
  • Explainability - Generate model explanations like SHAP values to understand features importance and model predictions.
  • Drift Detection - Automatically detect if your model performance changes over time due to concept drift in the underlying data.
  • Model Serving - Low-latency high-throughput serving using TensorFlow Serving under the hood.
  • Open Source - As an open source platform, Servo Labs is customizable, transparent, and free to use.

Overall, Servo Labs aims to bring MLOps best practices to model development workflows, helping data scientists avoid issues when deploying and monitoring models in production.

Servo Labs Features

Features

  1. Model versioning
  2. Model monitoring
  3. Model explainability
  4. Drift detection

Pricing

  • Open Source

Pros

Open source and free to use

Integrates with popular ML frameworks like TensorFlow, PyTorch, and scikit-learn

Helps manage models in production

Provides tools for monitoring and debugging models

Cons

Limited to machine learning models

Less flexible than writing custom model serving code

Requires some DevOps experience to setup and configure

Official Links

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