YelpArchive/MOE

Health v2 · maintenance only

Flags: archived no_license

Adoption not part of the score

1321 stars · 139 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MOE (Metric Optimization Engine) is a global, black box optimization engine built by Yelp for optimizing expensive, time-consuming objective functions using Bayesian global optimization and optimal learning. It is written in C++ with a Python interface and REST API, and is ideal when function evaluations are costly, derivatives are unavailable, and a global optimum is sought.

Use cases

• optimize click-through rate via A/B tests without wasting traffic
• tune hyperparameters of a slow machine-learning model
• optimize engineering designs that require expensive simulations
• optimize parameters of costly real-world lab experiments
• find global optima of non-convex black-box functions with no derivatives
• minimize the number of expensive objective function evaluations

When to choose

• each evaluation of your objective function is expensive or slow (A/B tests, simulations, lab experiments)
• the objective is a black box with no gradients and possibly non-convex
• you want Bayesian optimization with historical data support
• you need a REST API for an optimization service

When to avoid

• your objective function is cheap to evaluate (use standard gradient-based or scipy optimizers)
• you need gradients or convex optimization guarantees
• you need actively maintained software with up-to-date dependencies (CI is broken, last release 2023)
• you need a pure-Python lightweight library

Facets

library · maturity maintenance

machine-learning benchmarking machine-learning data-science python cpp bayesian-optimization black-box-optimization optimal-learning hyperparameter-optimization rest-api algorithms docker linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="YelpArchive/MOE")