Amazon Technologies, Inc.

Anand Dhandhania

Bhavesh Anil Doshi

Thomas Loockx

Andrea Olgiati

Rahul Bhotika

Amit Adam

Shai Mazor

Vasant Manohar

Vivek Bhadauria

Umar Farooq

Wendy Tse

Patrick Ian Wilson

Gururaj Kosuru

Descriptors of machine learning tasks to be used to respond to analysis requests, indicating acceptable categories of runtime environments for the tasks and metrics to be collected from the tasks, are received via programmatic interfaces. In response to an analysis request, an orchestrator receives results from individual tasks as they become available, provides the results to other tasks, and causes a response to the request to be prepared using results from at least a subset of the tasks. Metrics collected from the tasks, and a visual representation of the tasks indicating their runtime environments are presented.

Inference flow orchestration service

A network-based service may provide a machine learning model for different clients. The network-based service may implement an interface that allows a client to identify a test data set for validating versions of the machine learning model specifically for the client. When a new version of the machine learning model is created, a validation test using the test data set identified by the client may be used. Results of the validation test may be used to make a decision regard whether to migrate workloads for the client to the new version of the machine learning model.

Client-specific validation for migrating to a version of a machine learning model using client-supplied test data

Techniques for a framework for building, orchestrating, and deploying complex, large-scale Machine Learning (ML) or deep learning (DL) inference applications is described. A ML application orchestration service is disclosed that enables the construction, orchestration, and deployment of complex ML inference applications in a provider network. The disclosed service provides customers with the ability to define machine learning (ML) models and define transformation operations on data before and/or after being provided to the ML models to construct a complex ML inference application. The service provides a framework for the orchestration (co-ordination) of the workflow logic (e.g., of the request and/or response flows) involved in building and deploying a complex ML inference application in the provider network.

Framework for building, orchestrating and deploying large-scale machine learning applications

An API latency estimation system estimates latencies as a function of subcomponent parameters. The system may obtain first information indicative of at least a characteristic of data of a request provided to an API and second information indicative of at least a utilization of a first subcomponent of the API used to fulfill a subtask of a task of the request. An estimated latency for the first subcomponent to fulfill the subtask is determined at least in part by applying a latency estimation model for the API to at least the first information and the second information. If a comparison of the estimated latency to a measured latency for the first subcomponent to perform the subtask indicates a potential anomaly, then an indication of the potential anomaly may be outputted. The model may be updated with API request fulfillment data that is not anomalous.

Interface latency estimation based on platform subcomponent parameters

Techniques for layout-agnostic complex document processing are described. A document processing service can analyze documents that do not adhere to defined layout rules in an automated manner to determine the content and meaning of a variety of types of segments within the documents. The service may chunk a document into multiple chunks, and operate upon the chunks in parallel by identifying segments within each chunk, classifying the segments into segment types, and processing the segments using special-purpose analysis engines adapted for the analysis of particular segment types to generate results that can be aggregated into an overall output for the entire document that captures the meaning and context of the document text.

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Seattle, WA, United States of America

Anand Dhandhania