Amazon SageMaker | Respan Docs

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1 {
2   "mcpServers": {
3     "respan-docs": {
4       "url": "https://docs.respan.ai/mcp"
5     }
6   }
7 }

What is Amazon SageMaker?

Amazon SageMaker is AWS’s fully managed machine learning platform. It provides tools for building, training, and deploying ML models at scale, including real-time inference endpoints for LLMs and other models.

Setup

Install packages

$ pip install respan-ai opentelemetry-instrumentation-sagemaker boto3 python-dotenv

Set environment variables

$ export AWS_ACCESS_KEY_ID="YOUR_AWS_ACCESS_KEY_ID"
$ export AWS_SECRET_ACCESS_KEY="YOUR_AWS_SECRET_ACCESS_KEY"
$ export AWS_REGION="us-east-1"
$ export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
$ export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.respan.ai/api"
$ export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer $RESPAN_API_KEY"

Initialize and run

1 import os
2 import json
3 from dotenv import load_dotenv
4 
5 load_dotenv()
6 
7 import boto3
8 from respan import Respan
9 
10 # Auto-discover and activate all installed instrumentors
11 respan = Respan(is_auto_instrument=True)
12 
13 # Invoke a SageMaker endpoint — auto-traced by Respan
14 client = boto3.client("sagemaker-runtime", region_name=os.getenv("AWS_REGION"))
15 
16 payload = json.dumps({"inputs": "Say hello in three languages."})
17 
18 response = client.invoke_endpoint(
19     EndpointName="my-llm-endpoint",
20     ContentType="application/json",
21     Body=payload,
22 )
23 result = json.loads(response["Body"].read().decode())
24 print(result)
25 respan.flush()

View your trace

Open the Traces page to see your auto-instrumented endpoint invocation spans.

What gets traced

Endpoint name and model
Input/output payloads
Invocation latency
Token usage (for language model endpoints)
Error responses

Traces appear in the Traces dashboard.

Learn more

OpenTelemetry integration - How Respan processes OTel spans
Respan Python SDK - Full SDK reference