Abstract: Techniques for machine learning (ML) model training and deployment using model building blocks via graphical user interfaces (GUIs) are described. Users can use a GUI provided by an electronic device to select and configure ML aspects for one or more ML models to be trained using identified training data. The electronic device can send a request to cause a model construction service to train one or more ML models based on the user configuration, return results of the training to the user within the GUI, and deploy one or more of the ML models.

Abstract: Indications of sample machine learning models which create synthetic content items are provided via programmatic interfaces. A representation of a synthetic content item produced by one of the sample models in response to input obtained from a client of a provider network is presented. In response to a request from the client, a machine learning model is trained to produce additional synthetic content items.

Abstract: Techniques for generating and executing an execution plan for a machine learning (ML) model using one of an edge device and a non-edge device are described. In some examples, a request for the generation of the execution plan includes at least one objective for the execution of the ML model and the execution plan is generated based at least in part on comparative execution information and network latency information.

Abstract: A model selection and deployment service at a provider network receives an indication of sensor availability from a remote client device (e.g., what type of sensors are currently available to provide sensor data to the client device). The model selection and deployment service then selects, based on the sensor availability (and/or based on one or more other factors/criteria), a data processing model from a group of data processing models that are available for deployment to the client device. The model selection and deployment service then transmits the selected data processing model to the remote client device (e.g., for installation on the hub device). This may allow a client device to use the best data processing model for a sensor configuration and to dynamically adjust to any changes in the sensor configuration.

Abstract: Indications of sample machine learning models which create synthetic content items are provided via programmatic interfaces. A representation of a synthetic content item produced by one of the sample models in response to input obtained from a client of a provider network is presented. In response to a request from the client, a machine learning model is trained to produce additional synthetic content items.

Abstract: Techniques for utilizing adaptable filters for edge-based deep learning models are described. Filters may be utilized by an edge electronic device to filter elements of an input data stream so that only a subset of the elements are used as inputs to a machine learning model run by the electronic device, enabling successful operation despite the input data stream potentially being generated at a higher rate than a rate in which the ML model can be executed. The filter can be a differential-type filter that generates difference representations between consecutive elements of the data stream to determine which elements are to be passed on for the ML model, a “smart” filter such as a neural network trained using outputs from the ML model allowing the filter to “learn” which elements are the most likely to be of value to be passed on, or a combination of both.

Abstract: Techniques for intelligent coalescing of media streams are described. A coalesce engine receives multiple media streams, such as audio or video streams, that are misaligned. The coalesce engine can analyze the media streams by comparing representations of elements of the media streams to detect the misalignment. The coalesce engine may determine an offset amount representing the misalignment, and if the offset amount meets or exceeds a threshold the coalesce engine can work to eliminate the misalignment by introducing one or more artificial delays before sending elements of ones of the media streams that are “ahead” of others of the streams. The coalese engine can additionally or alternatively send feedback to sources of the media streams, causing the source(s) to attempt to mitigate the misalignment.

Abstract: Techniques for generating and executing an execution plan for a machine learning (ML) model using one of an edge device and a non-edge device are described. In some examples, a request for the generation of the execution plan includes at least one objective for the execution of the ML model and the execution plan is generated based at least in part on comparative execution information and network latency information.