# How do companies deploy and monitor machine learning models without a dedicated tool?

Category: MLOps Platforms. Intent: informational. Top brand: AWS.

Page: https://slateindex.ai/prompt/how-do-companies-deploy-and-monitor-machine-learning-models-x3d7yxwsoe · data of 2026-11-02

## Brands named

| Brand | Share of engines |
|---|---|
| Prometheus Group | 100% |
| Apache Airflow | 100% |
| AWS | 67% |
| GitHub | 67% |
| MLflow | 67% |
| Google Vertex AI | 67% |
| Weights & Biases | 33% |
| Amazon SageMaker AI | 33% |
| Anaconda | 33% |
| Evidently AI | 33% |
| Snowflake | 33% |
| Microsoft Azure Machine Learning | 33% |

## Sources

- amazon.com (3)
- anaconda.com (3)
- microsoft.com (2)
- ai.se (1)
- arxiv.org (1)
- datadoghq.com (1)
- google.com (1)
- gov.sg (1)
- ibm.com (1)
- mlflow.org (1)
- mlops.community (1)
- nvidia.com (1)
- openml.io (1)

## Definitions

- Visibility (0–100): how often and how high AI answers name the brand, weighted by position.
- Mention rate: share of collected AI answers that name the brand.
- Share of voice: the brand's mentions as a share of all brand mentions in the category.
- Citation authority: share of answers naming the brand that cite the brand's own site.

Cite as: SlateIndex, “How do companies deploy and monitor machine learning models without a dedicated tool?”, https://slateindex.ai/prompt/how-do-companies-deploy-and-monitor-machine-learning-models-x3d7yxwsoe, data of 2026-11-02. Licence: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).