Services or capabilities described in AWS documentation might vary by Region. To see the differences applicable to the AWS European Sovereign Cloud Region, see the AWS European Sovereign Cloud User Guide.

Sample datasets and tutorials

End-to-end example: Customer onboarding agent

The following example demonstrates the complete workflow for multi-turn RL: training an agent, evaluating its performance, and deploying the trained model.

from sagemaker.train.multi_turn_rl_trainer import MultiTurnRLTrainer
from sagemaker.train.evaluate import MultiTurnRLEvaluator
from sagemaker.serve import ModelBuilder
from sagemaker.core.resources import ModelPackage

DATASET = "s3://my-bucket/prompts/prompts.parquet"
AGENT_ARN = "arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/my-agent"

# ── 1. Train ──────────────────────────────────────────────────────────────
trainer = MultiTurnRLTrainer(
    model="openai-reasoning-gpt-oss-20b",
    agent_env=AGENT_ARN,
    training_dataset=DATASET,
    s3_output_path="s3://my-bucket/train-output/",
    accept_eula=True,
)
trainer.hyperparameters.max_epochs = 1
trainer.hyperparameters.global_batch_size = 10

job = trainer.train(wait=True)
print(f"Training complete: {job.output_model_package_arn}")

# ── 2. Evaluate ───────────────────────────────────────────────────────────
evaluator = MultiTurnRLEvaluator(
    model=trainer,
    dataset=DATASET,
    s3_output_path="s3://my-bucket/eval-output/",
    evaluate_base_model=True,  # Compare base vs fine-tuned
)
execution = evaluator.evaluate()
execution.wait()
print(f"Evaluation: {execution.status.overall_status}")

# ── 3. Deploy ─────────────────────────────────────────────────────────────
model_package = ModelPackage.get(model_package_name=job.output_model_package_arn)

model_builder = ModelBuilder(
    model=model_package,
    instance_type="ml.g6e.48xlarge",
)
model_builder.accept_eula = True
model_builder.build()

endpoint = model_builder.deploy(
    endpoint_name="mtrl-production-endpoint",
    instance_type="ml.g6e.48xlarge",
    initial_instance_count=1,
)
print(f"Deployed: {endpoint.endpoint_name}")