A control device for controlling a control target, comprising: a control logic configured to execute computations for controlling the control target; and a machine learning engine, wherein the control logic comprises: feature amount generating means configured to generate a feature amount suitable for detecting an anomaly that occurs in the control target from data that relates to the control target; and a machine learning engine interface, and the machine learning engine interface comprises: first instructing means configured to instruct the machine learning engine to perform machine learning, and data compressing means configured to data-compress the feature amount generated by the feature amount generating means, and provide the data-compressed feature amount to the machine learning engine, and the machine learning engine comprises: machine learning means configured to carry out machine learning using the data-compressed feature; and anomaly detecting means configured to detect the anomaly, based on the feature amount generated by the feature amount generating means and an anomaly detection parameter determined based on a learning result of the machine learning and used in detection of the anomaly that occurs in the control target, the machine learning engine interface further comprises second instructing means configured to instruct the anomaly detecting means to perform detection of the anomaly, the second instructing means is configured to transmit a request requiring detection of the anomaly to the anomaly detecting means, the anomaly detecting means is configured to carry out detection of the anomaly, without sending a response to the request to the second instructing means, and the machine learning interface is configured to carry on with subsequent processing without waiting for the response from the anomaly detecting means. a control logic configured to execute computations for controlling the control target; and a machine learning engine, wherein the control logic comprises: feature amount generating means configured to generate a feature amount suitable for detecting an anomaly that occurs in the control target from data that relates to the control target; and a machine learning engine interface, and the machine learning engine interface comprises: first instructing means configured to instruct the machine learning engine to perform machine learning, and data compressing means configured to data-compress the feature amount generated by the feature amount generating means, and provide the data-compressed feature amount to the machine learning engine, and feature amount generating means configured to generate a feature amount suitable for detecting an anomaly that occurs in the control target from data that relates to the control target; and a machine learning engine interface, and the machine learning engine interface comprises: first instructing means configured to instruct the machine learning engine to perform machine learning, and data compressing means configured to data-compress the feature amount generated by the feature amount generating means, and provide the data-compressed feature amount to the machine learning engine, and first instructing means configured to instruct the machine learning engine to perform machine learning, and data compressing means configured to data-compress the feature amount generated by the feature amount generating means, and provide the data-compressed feature amount to the machine learning engine, and the machine learning engine comprises: machine learning means configured to carry out machine learning using the data-compressed feature; and anomaly detecting means configured to detect the anomaly, based on the feature amount generated by the feature amount generating means and an anomaly detection parameter determined based on a learning result of the machine learning and used in detection of the anomaly that occurs in the control target, machine learning means configured to carry out machine learning using the data-compressed feature; and anomaly detecting means configured to detect the anomaly, based on the feature amount generated by the feature amount generating means and an anomaly detection parameter determined based on a learning result of the machine learning and used in detection of the anomaly that occurs in the control target, the machine learning engine interface further comprises second instructing means configured to instruct the anomaly detecting means to perform detection of the anomaly, the second instructing means is configured to transmit a request requiring detection of the anomaly to the anomaly detecting means, the anomaly detecting means is configured to carry out detection of the anomaly, without sending a response to the request to the second instructing means, and the machine learning interface is configured to carry on with subsequent processing without waiting for the response from the anomaly detecting means.