Domino Data Lab vs TensorBoard
Domino Data Lab
domino.aiTensorBoard
tensorflow.orgSide by side
| Figure | Domino Data Lab | TensorBoard |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 3 | 1 |
| Spec score · Experiment Tracking | 7.4 (#9) | 7.6 (#7) |
| Purpose | Domino’s Enterprise AI Platform brings together model development, MLOps, collaboration, and governance for enterprises building and operating AI at scale. | TensorBoard is a suite of visualization tools for understanding, debugging, and optimizing TensorFlow programs during machine learning experimentation. |
| Support | All plans include premium or enterprise support; the pricing page lists a two-business-day support SLA for Cloud and Premium and a one-business-day SLA for Enterprise. | The TensorBoard README directs general usage questions to Stack Overflow and bug reports to GitHub Issues. |
Plans
Domino Data Lab
- Domino CloudPriced on request
- EnterprisePriced on request
- PremiumPriced on request
In their own words
Domino Data Lab
Domino Data Lab has no write-up on Specifiction yet; its figures are above.
TensorBoard
TensorBoard is a free suite of visualization tools for understanding, debugging and optimizing TensorFlow programs during machine-learning experiments. It tracks metrics such as loss and accuracy, and its Graph Explorer displays model graphs for inspection. Dashboards show changes in tensor distributions through histograms and percentile summaries. The tools can also display logged images, audio and text, project high-dimensional embeddings and profile TensorFlow programs. In Keras workflows, a TensorFlow callback records metrics, training graph visualizations, weight histograms and sampled profiling data. TensorBoard can be launched from the command line or in notebooks using the `%tensorboard` line magic. It is designed for offline use, including local environments, corporate firewalls and datacenters. Version 2.0 and later defaults to `--host localhost`; `--bind_all` serves it on the public network over IPv4 and IPv6. Without a TensorFlow installation, it does not support log directories on Google Cloud Storage. The README identifies Chrome and Firefox as supported browsers and warns that others may have bugs or performance issues.