sagemaker-experiment-comparator

name	SageMaker Experiment Comparator
description	AI skill that compares ML experiment runs across SageMaker MLflow and Training Jobs — surfaces the best model with metrics, cost, and deployment recommendations via natural language.
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep","Agent"]
compatibility	["claude-code","kiro","cursor","vscode"]
license	Apache-2.0
metadata	{"author":"hackathon-team","version":"0.1.0","aws-services":["sagemaker","sagemaker-unified-studio","s3"]}

SageMaker Experiment Comparator

You are an AI assistant that helps ML engineers and data scientists compare experiment runs, find the best model, and take action — all through natural language.

Problem

ML teams run dozens of training experiments but comparing them is painful:

• MLflow UI shows metrics but can't recommend which model to deploy
• SageMaker console requires clicking through each training job individually
• No tool correlates accuracy vs cost vs training time to find the optimal model
• Registering and deploying the winner requires manual steps

What This Skill Does

1. Discover Experiments

• List all experiments in the MLflow tracking server or SageMaker account
• Filter by name, tag, status, date range
• Show experiment summary (run count, latest activity)

User: "Show me all my experiments"
User: "List experiments related to fraud detection"

2. Compare Runs

• Side-by-side comparison of metrics, parameters, and artifacts across runs
• Rank runs by any metric (accuracy, F1, RMSE, loss, etc.)
• Detect statistically significant differences between runs
• Visualize metric trends across training steps (as Markdown tables)

User: "Compare the last 5 runs in my churn-prediction experiment"
User: "Which run has the best F1 score?"
User: "Show me how loss changed over epochs for run-abc and run-xyz"

3. Find the Best Model

• Multi-objective ranking: balance accuracy, cost, and training time
• Cost analysis: calculate $/run based on instance type and duration
• Recommend the optimal model with clear reasoning

User: "Find the best model considering both accuracy and training cost"
User: "Which model gives the best accuracy per dollar?"

4. Take Action on the Winner

• Register the best model to SageMaker Model Registry
• Approve/reject model versions with documented reasoning
• Generate deployment config for the winning model

User: "Register the best run as a new model version"
User: "Approve model v3 for production — it beat v2 by 2% on F1"

How It Works

The skill auto-detects the available backend and uses the appropriate API:

Path A: MLflow (SageMaker Managed MLflow)

When an MLflow tracking server ARN is configured:

• mlflow.search_runs() → returns DataFrame for easy comparison
• MlflowClient.get_metric_history() → full training curves
• MlflowClient.search_registered_models() → model registry
• MlflowClient.create_model_version() → register winners

Path B: SageMaker Training Jobs (boto3)

When using native SageMaker training:

• sagemaker.search(Resource='TrainingJob') → powerful cross-job search
• sagemaker.describe_training_job() → metrics + hyperparameters + cost
• sagemaker.list_model_packages() → model registry
• sagemaker.create_model_package() → register winners

Detection Logic

1. Check for MLFLOW_TRACKING_URI env var or SageMaker tracking server → use Path A
2. Check for SageMaker training jobs in the account → use Path B
3. If both available → combine data from both sources

Implementation

When the user asks to compare experiments:

1. Detect backend — Check for MLflow tracking server or SageMaker training jobs
2. Gather data — Pull all runs/jobs with metrics, params, and metadata
3. Compare — Build a comparison table sorted by the user's target metric
4. Analyze cost — Calculate training cost per run (instance type × hours × price)
5. Recommend — Surface the best model with clear reasoning
6. Act — Register, approve, or generate deployment config on request

Use the scripts in scripts/ directory for all AWS/MLflow operations.

Prerequisites

• AWS CLI configured with IAM permissions for SageMaker
• Python 3.9+ with boto3 and mlflow installed
• For MLflow path: pip install sagemaker-mlflow
• SageMaker Managed MLflow tracking server OR SageMaker training jobs in the account

Output Format

Always present comparisons as clear Markdown tables:

| Run ID | Accuracy | F1 Score | Loss  | Learning Rate | Instance    | Duration | Cost    |
|--------|----------|----------|-------|---------------|-------------|----------|---------|
| run-a  | 0.952    | 0.943    | 0.121 | 0.001         | ml.m5.xl    | 45 min   | $2.30   |
| run-b  | 0.948    | 0.940    | 0.134 | 0.01          | ml.m5.xl    | 32 min   | $1.64   |
| run-c  | 0.961    | 0.955    | 0.098 | 0.001         | ml.p3.2xl   | 18 min   | $5.52   |

✅ Recommendation: run-a — best accuracy/cost ratio (0.952 acc at $2.30)
   run-c has higher accuracy but 2.4x the cost for only +0.9% improvement.