Theoretical, algorithmic and methodological aspects of stochastic modeling and technology efficiency control based on game-theoretic approach and machine learning are considered. The problem of assigning one of two ranks to a control object with the stochastic potential of technology is posed and solved for the case when probabilistic characteristics are known. Otherwise, to determine the optimal parameter of the ranking rule it is proposed to use the procedure of machine learning. The case of asymmetric awareness of the manager deciding on the ranking and the staff responsible for the effectiveness of the technology is considered. Far-sighted staff selects indicators of technology efficiency in such a way as to maximize own objective function, which depends on current and future results of ranking. There is a game between staff and manager which can lead to a decrease in the effectiveness of the technology and distortion of the estimates of the ranking parameters. This makes machine learning ineffective. To solve these problems, stochastic game model and ranking learning mechanism are proposed. The results of this mechanism functioning are estimates of ranking parameters, standards and ranks that determine staff stimuli. Sufficient conditions for the synthesis of ranking learning mechanism have been found, allowing to reveal the potential of technology effectiveness and to determine the optimal parameters of the ranking rule. These conditions are illustrated by the example of machine learning of ranking the technology electricity effectiveness in the process of implementing the program to increase the energy efficiency of the Russian Railways holding.