The correct answer is Orion AutoML service.

Orion AutoML service is the most effective choice because it is a managed, automated hyperparameter optimization solution that tightly integrates tuning with model training and evaluation. The service can orchestrate parallel trials, apply efficient search strategies under the hood, and pick the best configuration based on validation metrics, so it reduces manual effort and scales well for production workflows.

Grid Search is incorrect because it exhaustively evaluates combinations and quickly becomes infeasible for large or high dimensional search spaces, making it an inefficient choice for automated tuning.

Random Search is incorrect because while it can be simple and sometimes effective, it is not a managed, end to end service and it lacks the adaptive efficiency and orchestration that AutoML systems provide.

Bayesian Optimization is incorrect in this context because it describes an optimization technique rather than a standalone managed service, and teams typically use it as one algorithm inside an AutoML system rather than as the full automated solution itself.

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