Generative AI Fundamentals Accreditation: How to Use Model Serving and Lakehouse Monitoring for Generative AI Applications

Learn how Databricks Lakehouse AI features, such as Model Serving and Lakehouse Monitoring, can help you deploy and manage your generative AI models and data pipelines.

Multiple Choice Question

Which of these two Databricks Lakehouse AI features are used in the production phase of Generative AI applications? Choose 2 options.

A. Model Serving
B. Lakehouse Monitoring
C. Feature Serving
D. MLFlow Evaluation

Answer

A. Model Serving
B. Lakehouse Monitoring

Explanation

Model Serving is a feature of Databricks Lakehouse AI that allows customers to easily deploy and manage their generative AI models, such as large language models (LLMs), as scalable and reliable RESTful endpoints. Model Serving supports GPU-powered inference, which can significantly improve the performance and efficiency of LLMs. Model Serving also integrates with MLflow, a popular open source platform for the end-to-end machine learning lifecycle, to enable seamless tracking and management of model versions, experiments, and deployments.

Lakehouse Monitoring is a feature of Databricks Lakehouse AI that provides customers with end-to-end visibility into the data pipelines that drive their generative AI applications. Lakehouse Monitoring enables customers to monitor the quality, reliability, and freshness of their data, features, and models, as well as the performance, availability, and latency of their model serving endpoints. Lakehouse Monitoring also leverages MLflow to enable automatic lineage tracking and centralized permissions for all data, features, and models.

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