A computer-implemented method performed by a machine learning, ML, service of a provider network (100, 1100, 1200), the method comprising: receiving a first request message originated by a computing device of a user to identify a machine learning, ML, pipeline based at least in part on a dataset, the first request message identifying the dataset and an exploration budget; generating, based at least in part on the dataset, a plurality of ML pipeline plans, wherein each of the plurality of ML pipeline plans identifies one or more preprocessing stages and a ML model algorithm type; transmitting a first one or more messages to the computing device to cause the computing device to present information associated with the plurality of ML pipeline plans to the user, wherein the information, for each of the plurality of ML pipeline plans, identifies the ML pipeline plan; receiving a second request message originated by the computing device to begin an exploration of at least two of the plurality of ML pipeline plans; training, by a model training system of the ML service, based on at least a portion of the dataset and on the exploration budget, a plurality of ML models corresponding to the at least two ML pipeline plans; and transmitting a second one or more messages to the computing device of the user indicating a result of the training, receiving, by a model hosting system of the ML service, a third request message originated by the computing device of the user indicating a request to deploy an identified ML model corresponding to one of the at least two ML pipeline plans at an identified endpoint; deploying, by the model hosting system, the ML model at the identified endpoint, wherein the model hosting system initializes one or more ML containers in one or more hosted virtual machine instances, the one or more ML containers formed by the model hosting system from one or more container images stored in a ML container data store of the ML service, the one or more container images including executable instructions that represent an algorithm that defines the identified ML model, and wherein the identified endpoint is mapped by the model hosting system to a network address associated with the one or more ML containers; and storing a mapping between the identified endpoint and the network address at the model hosting system. receiving a first request message originated by a computing device of a user to identify a machine learning, ML, pipeline based at least in part on a dataset, the first request message identifying the dataset and an exploration budget; generating, based at least in part on the dataset, a plurality of ML pipeline plans, wherein each of the plurality of ML pipeline plans identifies one or more preprocessing stages and a ML model algorithm type; transmitting a first one or more messages to the computing device to cause the computing device to present information associated with the plurality of ML pipeline plans to the user, wherein the information, for each of the plurality of ML pipeline plans, identifies the ML pipeline plan; receiving a second request message originated by the computing device to begin an exploration of at least two of the plurality of ML pipeline plans; training, by a model training system of the ML service, based on at least a portion of the dataset and on the exploration budget, a plurality of ML models corresponding to the at least two ML pipeline plans; and transmitting a second one or more messages to the computing device of the user indicating a result of the training, receiving, by a model hosting system of the ML service, a third request message originated by the computing device of the user indicating a request to deploy an identified ML model corresponding to one of the at least two ML pipeline plans at an identified endpoint; deploying, by the model hosting system, the ML model at the identified endpoint, wherein the model hosting system initializes one or more ML containers in one or more hosted virtual machine instances, the one or more ML containers formed by the model hosting system from one or more container images stored in a ML container data store of the ML service, the one or more container images including executable instructions that represent an algorithm that defines the identified ML model, and wherein the identified endpoint is mapped by the model hosting system to a network address associated with the one or more ML containers; and storing a mapping between the identified endpoint and the network address at the model hosting system.