Elasticsearch's machine learning features, aimed at detecting anomalies in time series data, support various use cases such as identifying suspicious activities and planning routes. However, configuring optimal machine learning jobs can be complex due to the multitude of features available. To address this, Elasticsearch introduced automated job validation in Elastic Stack 6.3, enhanced in version 6.4, which provides detailed feedback on job configuration and links to relevant documentation. This feature, accessible on the jobs list page and within job creation wizards, helps users refine their settings by analyzing the configuration and underlying data, offering suggestions for improvements. Key aspects of job validation include checks on aggregatable fields, bucket span, time range, cardinality, and influencers, ensuring that users can preemptively address potential issues and optimize job configurations. An example illustrates how job validation spotted a typo in a configuration, leading to a series of adjustments that resulted in a successful validation, demonstrating how this tool aids in creating more effective analysis setups.