San Francisco – July 16,  2025

TAZI announced that it has been identified in Gartner’s Hype Cycle for Data Science and Machine Learning, 2025 under the Innovation Profile for Adaptive ML, authored by Pieter den Hamer and Erick Brethenoux. 

What “Adaptive ML” Means in Practice

Adaptive ML focuses on enabling machine learning models to be retrained frequently within their runtime environment, rather than only being trained offline during development. The aim is to help ML applications adapt faster when real-world conditions change. 

How TAZI Supports Adaptive ML

TAZI is designed to help business and technical teams operationalize adaptive ML workflows in a controlled way, including:

• Continuous learning readiness: supporting workflows where models can be updated as data and behaviors evolve.
• Operationalization: tooling to manage the cycle from monitoring to retraining and deployment within governed processes.
• Explainability for adoption: surfacing interpretable signals so business users can understand drivers and take action with confidence.
• Enterprise controls: supporting governance requirements for production ML, including repeatability and oversight.

Gartner subscribers can access the research here: https://www.gartner.com/en/documents/6727634 

Source: Gartner, Hype Cycle for Data Science and Machine Learning, 2025, Pieter den Hamer, Erick Brethenoux, July 15, 2025.

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