TECHNICAL FIELD

The subject matter of this disclosure relates generally generating a user interface for defining a machine learning compute request, and translating the machine learning compute request for processing the machine learning compute request on the cloud, and more specifically to receiving the machine learning request and a dataset through the user interface where the user interface does not require knowledge of how to set up specific machine learning projects at a public cloud.

BACKGROUND

As public cloud adoption continues to accelerate, mainstream applications and services are being migrated into the public cloud at a record pace. Each cloud platform provides hundreds of services, each of which requires specialized knowledge to utilize them. However, this brings substantial complexity and increasingly steep learning curves to migrate the applications into the cloud and utilize these cloud-native applications.

From a customer's perspective, there are two major barriers to utilize popular cloud services such as Machine Learning (ML). First, acquiring, transforming, and integrating all kinds of data for ML service is challenging because of the high cost associated with processing data for the customized ML service needs. Second, even if the data is ready for an ML service to process, the average customer still cannot utilize ML service for their own needs because of the specialized skill sets involved to operate ML services.

SUMMARY

A method includes: receiving a selected machine learning task and one or more file pointers, each file pointer indicates a network location; for each given file pointer of the one or more file pointers perform the processes of: ingesting input data from the network location indicated by the given file pointer, and labeling input data with a label generated based on the given file pointer and the selected machine learning task; then generating, a machine learning compute request, wherein the machine learning compute request includes a control information specifying one or more parameters for the selected machine learning task and a machine learning dataset generated from the labeled sequences of input data.

A non-transitory computer-readable storage medium having stored therein instructions which, when executed by one or more processors, can cause the one or more processors to: determine, based on a control information of a machine learning compute request and a type of the selected machine learning task, a cloud connector for the machine learning compute request; construct, a plurality of cloud services for the machine learning compute request, the constructing based at least in part on the control information of the machine learning compute request; and process, the machine learning compute request by providing a machine learning dataset to one or more of the constructed plurality of cloud services.

A system that includes one or more processors, and at least one computer-readable storage medium having stored therein instructions which, when executed by the one or more processors, can cause the one or more processors to: receive a selected machine learning task and one or more file pointers, each file pointer indicating a network location; for each given file pointer of the one or more file pointers: ingest, by at least one of local connecters, a sequence of input data from the network location indicated by the given file pointer; and label, by at least one of local connecters, the sequence of input data with a label generated based on the given file pointer and the selected machine learning task; and generate, by at least one of local connecters, a machine learning compute request, the machine learning compute request comprising a control information specifying one or more parameters for the selected machine learning task and a machine learning dataset generated from the labeled sequences of input data.

