An inference service deployment method, characterized by comprising: obtaining performance information of a runtime environment of a deployment end, wherein the performance information of the runtime environment comprises query time consuming at different quantile levels; selecting, according to the performance information of the runtime environment of the deployment end, a target version of an inference service from a plurality of candidate versions of the inference service of a model; and deploying the target version of the inference service to the deployment end, comprising: determining a resource amount corresponding to the target version of the inference service and a configuration parameter corresponding to the inference service; determining a quantity of copies and an external access address of the target version of the inference service; and launching the target version of the inference service online to the deployment end, based on the resource amount corresponding to the target version of the inference service, the configuration parameter corresponding to the inference service, the quantity of copies and the external access address; updating the inference service that has been deployed at the deployment end, comprising: updating a new version of the inference service that has been deployed at the deployment end by a specified quantity of copies in a rolling manner, to gradually replace an old version of the inference service that has been deployed at the deployment end; performing data collection for the inference service that has been deployed at the deployment end, to obtain statistical information of a runtime effect of the model, wherein the statistical information comprises at least one of: a query data distribution offset of the model in different periods of a model runtime environment, a data distribution offset of the model between query data in the model runtime environment and model training data, or a comparison result of the model between an output result in the model runtime environment and a labeling result; and judging, according to the statistical information, whether the model is suitable for the model runtime environment. obtaining performance information of a runtime environment of a deployment end, wherein the performance information of the runtime environment comprises query time consuming at different quantile levels; selecting, according to the performance information of the runtime environment of the deployment end, a target version of an inference service from a plurality of candidate versions of the inference service of a model; and deploying the target version of the inference service to the deployment end, comprising: determining a resource amount corresponding to the target version of the inference service and a configuration parameter corresponding to the inference service; determining a quantity of copies and an external access address of the target version of the inference service; and launching the target version of the inference service online to the deployment end, based on the resource amount corresponding to the target version of the inference service, the configuration parameter corresponding to the inference service, the quantity of copies and the external access address; determining a resource amount corresponding to the target version of the inference service and a configuration parameter corresponding to the inference service; determining a quantity of copies and an external access address of the target version of the inference service; and launching the target version of the inference service online to the deployment end, based on the resource amount corresponding to the target version of the inference service, the configuration parameter corresponding to the inference service, the quantity of copies and the external access address; updating the inference service that has been deployed at the deployment end, comprising: updating a new version of the inference service that has been deployed at the deployment end by a specified quantity of copies in a rolling manner, to gradually replace an old version of the inference service that has been deployed at the deployment end; performing data collection for the inference service that has been deployed at the deployment end, to obtain statistical information of a runtime effect of the model, wherein the statistical information comprises at least one of: a query data distribution offset of the model in different periods of a model runtime environment, a data distribution offset of the model between query data in the model runtime environment and model training data, or a comparison result of the model between an output result in the model runtime environment and a labeling result; and judging, according to the statistical information, whether the model is suitable for the model runtime environment.