Monitoring Model Lifecycles
The discussion highlights the strategic choice to prioritize a broader monitoring approach, allowing users to engage with the tool at various stages of the model lifecycle. Users are leveraging the tool even before model creation, using it to analyze historical data, which fosters early adoption and sets the stage for future monitoring needs. This engagement strategy is seen as a pathway to convert early users into long-term customers.In this clip
From this podcast
Open Source Startup Podcast
E33: Evidently AI and Open Source Machine Learning Monitoring
Related Questions
Can technology track real-time changes as discussed in the episode MLOps Meetup #23 // Monitoring the ML stack // Lina Weichbrodt and the clip Real-Time Monitoring?
Can you tell me about open monitoring in the context of the episode MLOps Meetup #34: Streaming Machine Learning with Apache Kafka and Tiered Storage // Kai Waehner, Confluent and the clip Real-Time Monitoring?