Learn about the features and functionalities of Databricks Lakehouse AI, a platform that provides one unified toolkit for building and serving AI models, with integrated capabilities for the entire AI lifecycle.
Table of Contents
Multiple Choice Question
Databricks Lakehouse AI provides one unified toolkit for building and serving AI models. This unified platform has many integrated AI capabilities.
Which statement correctly describes the capability and functionality of Databricks Lakehouse AI feature? Choose 2 options.
A. Unity Catalog offers governance and lineage for data and AI resources.
B. MLFlow AI Gateway enables comparing multiple models against each other and observing responses.
C. AI model development supports open-source models only.
D. Lakehouse Monitoring enables the tracking of AI model logs, ensuring continuous improvement and optimization.
Answer
A. Unity Catalog offers governance and lineage for data and AI resources.
D. Lakehouse Monitoring enables the tracking of AI model logs, ensuring continuous improvement and optimization.
Explanation
Databricks Lakehouse AI is a platform that provides one unified toolkit for building and serving AI models, with integrated capabilities for the entire AI lifecycle, such as data preparation, feature engineering, model training, deployment, monitoring, and governance. Databricks Lakehouse AI leverages the lakehouse architecture, which combines the best of data warehouses and data lakes, to enable customers to develop and manage their generative AI applications with speed, reliability, and full governance.
Some of the features and functionalities of Databricks Lakehouse AI are:
- Unity Catalog is a feature that offers governance and lineage for data and AI resources, such as data, features, and models. Unity Catalog enables customers to manage all their data and AI assets in one place, with automatic lineage tracking, centralized permissions, and seamless collaboration. Unity Catalog also integrates with MLflow, an open source platform for the end-to-end machine learning lifecycle, to enable unified management of model versions, experiments, and deployments.
- Lakehouse Monitoring is a feature that enables the tracking of AI model logs, ensuring continuous improvement and optimization. Lakehouse Monitoring provides customers with end-to-end visibility into the data pipelines that drive their generative AI applications, such as data quality, reliability, and freshness, as well as model performance, availability, and latency. Lakehouse Monitoring also leverages MLflow to enable automatic lineage tracking and centralized permissions for all data, features, and models.
The other two options are incorrect because:
- MLFlow AI Gateway is a feature that enables prompt engineering and evaluation for large language models (LLMs), such as BERT or GPT-3. MLFlow AI Gateway allows customers to easily compare multiple models against each other and observe responses, as well as to create and test different prompts for their models, such as instructions, commands, or questions. MLFlow AI Gateway also integrates with MLflow to enable seamless tracking and management of model versions, experiments, and deployments.
- AI model development is a capability that supports both open-source and proprietary models, as well as hybrid models that combine both. AI model development enables customers to use a curated collection of open-source models, such as MosaicML’s MPT-7B, available in the marketplace, or to train their own custom models securely with their enterprise data, using Databricks’ scalable and reliable infrastructure. AI model development also supports GPU-powered inference, which can significantly improve the performance and efficiency of LLMs.
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