Machine learning repository service
Techniques for providing and servicing listed repository items such as algorithms, data, models, pipelines, and/or notebooks are described. In some examples, web services provider receives a request for a listed repository item from a requester, the request indicating at least a category of the repository item and each listing of a repository item includes an indication of a category that the listed repository item belongs to and a storage location of the listed repository item, determines a suggestion of at least one listed repository item based on the request, and provides the suggestion of the at least one listed repository item to the requester.
1 . A computer-implemented method comprising:
publishing, by a web services provider, hosted machine learning models to a hosted machine learning repository, at least some of the hosted machine learning models including a name of the hosted machine learning model and an indication of a category to which the hosted machine learning model belongs;
receiving, by the web services provider from a requester via an application programming interface (API) frontend, a query specifying a requested category of machine learning models;
determining, by the web services provider, one or more of the hosted machine learning models that correspond to the requested category;
returning, by the web services provider to the requester via the API frontend, the one or more of the hosted machine learning models that correspond to the requested category;
receiving, by the web services provider from the requester via the API frontend, a request to use a selected machine learning model from among the one or more of the hosted machine learning models that correspond to the requested category;
adding, by the web services provider, the selected machine learning model as part of a pipeline;
allocating, by the web services provider, execution resources to the pipeline;
receiving, by the web services provider from the requester via the API frontend, a request to perform a task using the selected machine learning model; and
performing, by the web services provider in response to the request to perform a task, the task using the selected machine learning model and the execution resources.
2 . The computer-implemented method of claim 1 , the hosted machine learning repository hosted by the web services provider.
3 . The computer-implemented method of claim 1 , further comprising:
obtaining access rights information from a user account associated with the requester; and
determining access rights for the requester based on the access rights information.
4 . The computer-implemented method of claim 1 , the execution resources including compute resources and storage.
5 . The computer-implemented method of claim 1 , the task comprising training the selected machine learning model.
6 . The computer-implemented method of claim 1 , the task comprising performing inference using the selected machine learning model.
7 . The computer-implemented method of claim 1 , at least some of the hosted machine learning models further including an indication of an input format for the hosted machine learning model.
8 . A computer-implemented method comprising:
publishing, by a web services provider, hosted machine learning models to a hosted machine learning repository, at least some of the hosted machine learning models including a name of the hosted machine learning model and an indication of a category to which the hosted machine learning model belongs;
receiving, by the web services provider from a requester via an application programming interface (API) frontend, a query specifying a name of a requested machine learning model;
determining, by the web services provider, one or more of the hosted machine learning models that correspond to the name of the requested machine learning model;
returning, by the web services provider to the requester via the API frontend, the one or more of the hosted machine learning models that correspond to the name of the requested machine learning model;
receiving, by the web services provider from the requester via the API frontend, a request to use a selected machine learning model from among the one or more of the hosted machine learning models that correspond to the name of the requested machine learning model;
adding, by the web services provider, the selected machine learning model as part of a pipeline;
allocating, by the web services provider, execution resources to the pipeline;
receiving, by the web services provider from the requester via the API frontend, a request to perform a task using the selected machine learning model; and
performing, by the web services provider in response to the request to perform a task, the task using the selected machine learning model and the execution resources.
9 . The computer-implemented method of claim 8 , the hosted machine learning repository hosted by the web services provider.
10 . The computer-implemented method of claim 8 , further comprising:
obtaining access rights information from a user account associated with the requester; and
determining access rights for the requester based on the access rights information.
11 . The computer-implemented method of claim 8 , the execution resources including compute resources and storage.
12 . The computer-implemented method of claim 8 , the task comprising training the selected machine learning model.
13 . The computer-implemented method of claim 8 , the task comprising performing inference using the selected machine learning model.
14 . The computer-implemented method of claim 8 , at least some of the hosted machine learning models further including an indication of an input format for the hosted machine learning model.
15 . A system comprising:
a hosted machine learning repository; and
a web services provider including memory storing instructions that, when executed by at least one processor of the web services provider, cause the web services provider to:
publish hosted machine learning models to the hosted machine learning repository, at least some of the hosted machine learning models including a name of the hosted machine learning model and an indication of a category to which the hosted machine learning model belongs;
receive, from a requester via an application programming interface (API) frontend, a query specifying a requested category of machine learning models;
determine one or more of the hosted machine learning models that correspond to the requested category;
return, to the requester via the API frontend, the one or more of the hosted machine learning models that correspond to the requested category;
receive, from the requester via the API frontend, a request to use a selected machine learning model from among the one or more of the hosted machine learning models that correspond to the requested category;
add the selected machine learning model as part of a pipeline;
allocate execution resources to the pipeline;
receive, from the requester via the API frontend, a request to perform a task using the selected machine learning model; and
perform, in response to the request to perform a task, the task using the selected machine learning model and the execution resources.
16 . The system of claim 15 , the hosted machine learning repository hosted by the web services provider.
17 . The system of claim 15 , the memory storing further instructions that, when executed by the at least one processor of the web services provider, further cause the web services provider to:
obtain access rights information from a user account associated with the requester; and
determine access rights for the requester based on the access rights information.
18 . The system of claim 15 , the execution resources including compute resources and storage.
19 . The system of claim 15 , the task comprising training the selected machine learning model.
20 . The system of claim 15 , the task comprising performing inference using the selected machine learning model.