An apparatus and method for providing ML processing for one or more ML applications operating on one or more Internet of Things (IoT) devices includes receiving a ML request from an IoT device. The ML request can be generated by a ML application operating on the IoT device and include input data collected by the first ML application. A ML model to perform ML processing of the input data included in the ML request is identified and provided to an ML core for ML processing along with the input data included in the first ML request. The ML core produces ML processing output data based on ML processing by the ML core of input data included in the ML request using the ML model. The ML processing output data can be transmitted to the IoT device.

The invention enables high-availability, high-scale, high security and disaster recovery for API computing, including in terms of capture of data traffic passing through proxies, routing communications between clients and servers, and load balancing and/or forwarding functions. The invention inter alia provides (i) a scalable cluster of proxies configured to route communications between clients and servers, without any single point of failure, (ii) proxy nodes configured for implementing the scalable cluster (iii) efficient methods of configuring the proxy cluster, (iv) natural resiliency of clusters and/or proxy nodes within a cluster, (v) methods for scaling of clusters, (vi) configurability of clusters to span multiple servers, multiple racks and multiple datacenters, thereby ensuring high availability and disaster recovery (vii) switching between proxies or between servers without loss of session.

Extending access to a data model in a data analytics computer data processing system includes loading into a programmatically isolated process address space of a computer, an instance of an extension framework computer program and executing in the framework, computer program logic configured to establish a communicative channel between the isolated process address space and a data analytics computer data processing system executing in a separate process address space. Thereafter, within the framework a directive may be received to access a data model managed in the data analytics computer data processing system. In response, a function may be selected in respect to an API to the data analytics computer data processing system corresponding to the received directive. Finally, the selected API function may be invoked over the communicative channel and a result derived from the data model may be received in the framework from over the communicative channel in response to the selected API function.

Extending access to a data model in a data analytics computer data processing system includes loading into a programmatically isolated process address space of a computer, an instance of an extension framework computer program and executing in the framework, computer program logic configured to establish a communicative channel between the isolated process address space and a data analytics computer data processing system executing in a separate process address space. Thereafter, within the framework a directive may be received to access a data model managed in the data analytics computer data processing system. In response, a function may be selected in respect to an API to the data analytics computer data processing system corresponding to the received directive. Finally, the selected API function may be invoked over the communicative channel and a result derived from the data model may be received in the framework from over the communicative channel in response to the selected API function.

A method of traffic reduction in a mesh computing system (400), the mesh computing system (400) comprising hosts located on edge nodes of the mesh computing system (400) and a central registry located outside the mesh computing system (400), the central registry holding the images. The method comprises, at a first host located at a first edge node, receiving (920) a request from a client for an image, sending (930) a request for the image to at least one other host of the mesh computing system (400). When the first host receives (940) notification that at least a second host holds the image, the first host downloads (960) the image from the second host to the first host. The first host creates (970) a container from the image. A host at a node (636; 700) and a mesh computing system (400) are also provided.

A method of traffic reduction in a mesh computing system (400), the mesh computing system (400) comprising hosts located on edge nodes of the mesh computing system (400) and a central registry located outside the mesh computing system (400), the central registry holding the images. The method comprises, at a first host located at a first edge node, receiving (920) a request from a client for an image, sending (930) a request for the image to at least one other host of the mesh computing system (400). When the first host receives (940) notification that at least a second host holds the image, the first host downloads (960) the image from the second host to the first host. The first host creates (970) a container from the image. A host at a node (636; 700) and a mesh computing system (400) are also provided.

A method of hosting a single page application incudes hosting, at an application programming interface (API) module of a server, the single page application as a first API operation by providing code to a client device to enable rendering of a page at the client device as a user interface presentation.

Various example embodiments for supporting device telemetry control are presented. Various example embodiments may provide a customer of a device, which is monitoring the device based on device telemetry whereby the device exposes device data of the device based on device telemetry control information of the device such that the data of the device may be accessed by the customer, with control over device telemetry of the device. Various example embodiments may provide a customer, which may access device data of a device based on device telemetry supported by the device, with additional control over access to the device data of the device via device telemetry by providing the customer with control over the device telemetry including enabling the customer to insert customer device telemetry control information into the device telemetry control information of the device that controls device telemetry on the device.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

A method for use with a computing device. The method may include receiving a data set including a plurality of univariate data points and determining a target kernel bandwidth for a kernel density estimator (KDE). Determining the target kernel bandwidth may include computing a plurality of sample KDEs and selecting the target kernel bandwidth based on the sample KDEs. The method may further include computing the KDE for the data set using the target kernel bandwidth. For one or more tail regions of the data set, the method may further include computing one or more respective tail extensions. The method may further include computing and outputting a renormalized piecewise density estimator that, in each tail region, equals a renormalization of the respective tail extension for that tail region, and, outside the one or more tail regions, equals a renormalization of the KDE.