A method for identifying a problematic compute resource via a dependency graph that comprises a plurality of nodes (302, 304, 306, 308, 310, 312, 314, 316, 318) representing a plurality of compute resources, the method comprising: for each compute resource represented by a node of the dependency graph: providing features associated with the compute resource to a generated machine learning model configured to output a score for the compute resource, the score indicating a likelihood that the compute resource is problematic, wherein generating the machine learning model comprises determining at least one user from which first training data is to be collected, and wherein determining the at least one user comprises: for each user of a plurality of users: determining that a number of debug sessions in which the user has engaged exceeds a predetermined threshold; and in response to determining that the number of debug sessions in which the at least one user has engaged has exceeded the predetermined threshold, determining that the first training data is to be collected from the user; and for each node associated with a score that exceeds a predetermined threshold: providing focus to at least one of the node or an edge coupling the at least one node to another node of the dependency graph that represents another compute resource. providing features associated with the compute resource to a generated machine learning model configured to output a score for the compute resource, the score indicating a likelihood that the compute resource is problematic, wherein generating the machine learning model comprises determining at least one user from which first training data is to be collected, and wherein determining the at least one user comprises: for each user of a plurality of users: determining that a number of debug sessions in which the user has engaged exceeds a predetermined threshold; and in response to determining that the number of debug sessions in which the at least one user has engaged has exceeded the predetermined threshold, determining that the first training data is to be collected from the user; and for each node associated with a score that exceeds a predetermined threshold: providing focus to at least one of the node or an edge coupling the at least one node to another node of the dependency graph that represents another compute resource. for each user of a plurality of users: determining that a number of debug sessions in which the user has engaged exceeds a predetermined threshold; and in response to determining that the number of debug sessions in which the at least one user has engaged has exceeded the predetermined threshold, determining that the first training data is to be collected from the user; and determining that a number of debug sessions in which the user has engaged exceeds a predetermined threshold; and in response to determining that the number of debug sessions in which the at least one user has engaged has exceeded the predetermined threshold, determining that the first training data is to be collected from the user; and for each node associated with a score that exceeds a predetermined threshold: providing focus to at least one of the node or an edge coupling the at least one node to another node of the dependency graph that represents another compute resource.