Abstract: Embodiments provide for improved model maintenance utilizing third-party workspaces. Some embodiments receive data artifact(s) associated with training of a machine learning model, generate model keyword(s) based on the data artifact(s), and store the machine learning model linked with the model keyword(s). Some embodiments receive data artifact(s), generate an embedded representation based on the data artifact(s), and store the embedded representation of the machine learning model in an embedding space. The stored data is then searchable to identify relevant models for deployment.

Abstract: Embodiments provide for improved model maintenance utilizing third-party workspaces. Some embodiments receive data artifact(s) associated with training of a machine learning model, generate model keyword(s) based on the data artifact(s), and store the machine learning model linked with the model keyword(s). Some embodiments receive data artifact(s), generate an embedded representation based on the data artifact(s), and store the embedded representation of the machine learning model in an embedding space. The stored data is then searchable to identify relevant models for deployment.

Abstract: Embodiments provide for improved model maintenance utilizing third-party workspaces. Some embodiments receive data artifact(s) associated with training of a machine learning model, generate model keyword(s) based on the data artifact(s), and store the machine learning model linked with the model keyword(s). Some embodiments receive data artifact(s), generate an embedded representation based on the data artifact(s), and store the embedded representation of the machine learning model in an embedding space. The stored data is then searchable to identify relevant models for deployment.

Abstract: Various embodiments of the present disclosure provide machine learning configuration techniques for seamlessly leveraging compute functionalities from across a plurality of disparate third-party computing resources. The configuration techniques include receiving a first-party workspace request that identifies a third-party computing resource and in response to the first-party workspace request: generating a compute agnostic project workspace hosted by a first-party computing resource, initiating the generation of a third-party workspace hosted by the third-party computing resource, and initiating the configuration of a first-party routine set within the third-party workspace. The first-party routine set includes a plurality of webhooks that facilitate communication between the first-party computing resource and the third-party computing resource, thereby enabling a first-party to leverage multiple different, traditionally incompatible, computing functionalities from one centralized location.

Abstract: Various embodiments of the present disclosure provide universal machine learning tracking and control techniques for enforcing universal standards across a plurality of disparate machine learning projects within an enterprise. The techniques include generating a canonical representation of a machine learning model. The techniques include receiving model activity data from a third party computing resource in response to user activity within a third party workspace. The techniques include generating relative progress data for the machine learning model based on the model activity data and modifying the canonical representation of the machine learning model based on the model activity data and the relative progress data. The techniques include generating and providing a model interface point for the machine learning model in response to the canonical representation satisfying a publication threshold.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.