AS
aws/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
10.9k 7.0k +0/wk
GitHub
aws data-science deep-learning examples inference jupyter-notebook machine-learning mlops reinforcement-learning sagemaker training
Trend
3
Star & Fork Trend (49 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
aws/amazon-sagemaker-examples has +0 stars this period . 7-day velocity: 0.0%.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | amazon-sagemaker-examples | dopamine | wandb | MLAlgorithms |
|---|---|---|---|---|
| Stars | 10.9k | 10.9k | 11.0k | 11.0k |
| Forks | 7.0k | 1.4k | 856 | 1.8k |
| Weekly Growth | +0 | +0 | -3 | +1 |
| Language | Jupyter Notebook | Jupyter Notebook | Python | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | Apache-2.0 | Apache-2.0 | MIT | MIT |
Capability Radar vs dopamine
amazon-sagemaker-examples
dopamine
Maintenance Activity 100
Last code push 3 days ago.
Community Engagement 20
Fork-to-star ratio: 64.1%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under Apache-2.0. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.