Abstract: In general, techniques for efficient data retrieval from a backup system are described. An example computing system includes one or more storage devices and processing circuitry having access to the one or more storage devices and configured to: process an input to generate a filter, wherein the input indicates a context for one or more queries; apply the filter to backup data to obtain filtered data from the backup data; generate an index of embeddings from the filtered data; process, based on the index of embeddings, a query to generate a response for the query; and output the response.

Abstract: In general, various aspects of the techniques enable a computing system to implement a unified metadata catalog. The computing system may include a memory configured to store a unified metadata catalog processing circuitry. The processing circuitry may be configured to obtain metadata from data objects, and log the metadata to the unified metadata catalog. The unified metadata catalog may log the metadata as the metadata changes over time. The processing circuitry may be further configured to expose the unified metadata catalog to one or more services that perform operations with respect to the unified metadata catalog.

Abstract: In general, techniques for dataset clustering and artificial intelligence (AI)-assisted theme extraction are described. In an example, a method comprises computing, by a data management platform, chunk embeddings for respective chunks obtained from a dataset; generating, by the data management platform, based on the chunk embeddings, a cluster hierarchy having a plurality of clusters, each cluster of the plurality of clusters including one or more of the chunk embeddings; generating, by the data management platform, using a machine learning model, a theme for a cluster of the plurality of clusters, the theme generated by the machine learning model based on respective chunks of the one or more of the chunk embeddings included in the cluster; and outputting, by the data management platform, an indication of the theme for the cluster.

Abstract: In an example, a method comprises generating, with a computing system-executed AI agent applying a machine learning model, based on a query associated with a user, an execution plan for a task to satisfy the query, wherein the execution plan includes actions to be performed with respect to a first data source system and a second data source system, and wherein the user has permission for each of the actions; invoking, by the AI agent, a first tool to perform a first action of the actions with respect to the first data source system, wherein the AI agent is trained to use the first tool; and invoking, by the AI agent, a second tool to perform a second action of the actions with respect to the second data source system, wherein the AI agent is trained to use the second tool.

Abstract: In general, techniques for efficient data retrieval from a backup system are described. An example computing system includes one or more storage devices and processing circuitry having access to the one or more storage devices and configured to: process an input to generate a filter, wherein the input indicates a context for one or more queries; apply the filter to backup data to obtain filtered data from the backup data; generate an index of embeddings from the filtered data; process, based on the index of embeddings, a query to generate a response for the query; and output the response.