Abstract: An example operation may include one or more of executing a worker on a server host, wherein the worker comprises a log messenger and at least one processing thread, executing a message broker on the server host, wherein the message broker is decoupled from the worker and comprises a queue, executing at least one artificial intelligence (AI) pipeline via the at least one processing thread of the worker, wherein the at least one AI pipeline is configured to generate log messages, transferring the log messages from the log messenger to the message broker, storing the log messages within the queue of the message broker, determining, by the message broker, that a log message from among the log messages has reached an end of the queue, and in response to a determination that the log message has reached the end of the queue, writing, by the message broker, the log message to a log database.

Abstract: AI models such as boosted decision trees, neural networks, large language models (LLMs), and agentic systems are deployed with multiple multi-threaded workers to service requests in parallel. The processing configuration is analyzed to determine distance between thread execution units (e.g., vCPUs) and a mapping determined to optimize thread assignment to the thread execution units. The threads may be statically pinned to particular vCPUs to prevent thread migration and optimize throughput. The thread mapping accounts for thread workloads and core-to-core distance. The mapping may seek to maximize distance between worker main threads and may either minimize (compact) or maximize (spread) distance between threads within the same worker. The mapping may be determined as an optimization problem, such as an Integer Linear Problem (ILP) that defines an objective and various constraints for optimization.

Abstract: An example operation may include one or more of storing utility components for a production environment within a storage, receiving, via a software application, an AI model from a development environment, receiving a configuration file defining a configuration of a pipeline in the production environment which includes the AI model, generating, via the software application, a production pipeline of the AI model which includes a sequence of components including the AI model and at least one utility component based on the configuration file, determining whether the production pipeline satisfies at least one regulatory requirement based on the sequence of components, in response to the production pipeline satisfying the at least one regulatory requirement, executing the production pipeline on input data via the software application in the production environment and may further include an AI agent updating the configuration file of the production pipeline satisfying the at least one regulatory requirement.

Abstract: An example operation may include one or more of storing utility components within a storage of a software application, receiving a configuration file defining component configurations, receiving an artificial intelligence (AI) model which comprises at least one of source code, binary code, software end points, and configuration information, wrapping the AI model into a wrapped AI model that includes an interface that provides access to the AI model, generating a production pipeline for the AI model which includes a sequence of components including the wrapped AI model and at least one utility component connected to the interface based on the component configurations included in the configuration file, and executing the production pipeline for the AI model on input data via the software application to generate an inference result. The example operation may further include an AI agent modifying the configuration file for the production pipeline to generate the inference result.

Abstract: An example operation may include one or more of executing a software application via a plurality of cores of a server host, testing the software application while executing the software application via the plurality of cores of the server host, capturing runtime attributes of the software application based on the testing, determining, via an optimizer, an optimal subset of cores for executing the software application from among the plurality of cores of the server host based on the runtime attributes of the software application, dynamically assigning, via the optimizer, the software application to the optimal subset of cores, and modifying the server host to execute the software application on the optimal subset of cores. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and/or is associated with an AI model.

Abstract: An example operation may include one or more of executing a software application on a plurality of different cores, identifying runtime attributes of the plurality of different cores while executing the software application, identifying current processing loads of the plurality of different cores, adding a weight to the runtime attributes of the plurality of different cores to generate weighted runtime attributes, assigning the software application to a subset of cores from among the plurality of different cores based on the weighted runtime attributes and the current processing loads of the plurality of different cores, and updating the executing of the software application to execute the software application via the subset of cores. An artificial intelligence (AI) model can be trained and/or executed when performing at least one portion of the example operation.

Abstract: An example operation may include one or more of executing a worker on a server host, wherein the worker comprises a log messenger, an application programming interface (API), and at least one processing thread, executing a predictive pipeline using the at least one processing thread of the worker, wherein the predictive pipeline comprises an artificial intelligence (AI) model and at least one pipeline attribute, identifying at least one load attribute of the server host, generating a logging system configured to receive log messages of the predictive pipeline from the log messenger and write the log messages to a log database, determining whether to deploy the logging system at the server host or at a remote host based on the at least one load attribute of the server host and the at least one pipeline attribute of the predictive pipeline, and deploying the logging system based on the determining of whether to deploy the logging system at the server host or the remote host.

Abstract: An example operation may include one or more of executing a worker on a server host, wherein the worker comprises a log messenger and at least one processing thread, executing a message broker on the server host, wherein the message broker is decoupled from the worker and comprises a queue, executing at least one artificial intelligence (AI) pipeline via the at least one processing thread of the worker, wherein the at least one AI pipeline is configured to generate log messages, transferring the log messages from the log messenger to the message broker, storing the log messages within the queue of the message broker, determining, by the message broker, that a log message from among the log messages has reached an end of the queue, and in response to a determination that the log message has reached the end of the queue, writing, by the message broker, the log message to a log database.

Abstract: An example operation may include one or more of running an optimization module on an optimizer host, assigning, by the optimizer host, a software application to at least one core from among a plurality of cores of a server host, instructing, by the optimizer host, the server host to process the software application using the at least one core, determining, by the optimizer host, runtime attributes of the software application while it is executing on the at least one core based on data fed back from the server host, dynamically re-assigning the software application to at least one different core from among the plurality of cores of the server host based on the runtime attributes, and re-instructing, by the optimizer host, the server host to execute the software application on the at least one different core instead of the at least one core. An artificial intelligence (AI) model can be trained and/or executed when performing at least one portion of the example operation.

Abstract: Systems and methods are provided for a machine learning (ML) pipeline with a unified framework. A machine learning pipeline trains a machine learning model in the machine learning pipeline in a development environment to generate training artifacts. The machine learning pipeline further executes the machine learning model in a production environment to generate production artifacts. When a training data logger and the machine learning pipeline are in communication with each other, the machine learning pipeline automatically transmits the training artifacts to the training data logger for storage in the development environment. When the production data logger and the machine learning pipeline are in communication with each other, the machine learning pipeline automatically transmits the production artifacts to the production data logger for storage in the production environment.

Abstract: Systems and methods are provided for a machine learning (ML) pipeline with a unified framework. A training data adapter receives training data in a training data format, processes the training data to match a pipeline data format of a machine learning pipeline, and transmits reformatted training data to the machine learning pipeline. The machine learning pipeline receives and processes the reformatted training data to train a machine learning model in the machine learning pipeline in a development environment, and further executes the machine learning model in a production environment. An artifact adapter receives training artifacts that were produced while training the machine learning model, and processes the training artifacts to update the machine learning model in the machine learning pipeline.

Abstract: Systems and methods are provided for a machine learning (ML) pipeline with a unified framework. A ML pipeline trains a ML model in a development environment, and further executes the ML model in a production environment. A data storage in the development environment stores training performance metrics corresponding to the training of the machine learning model. A development web server in the development environment retrieves the training performance metrics from the data storage in the development environment, and presents the training performance metrics. A data storage in the production environment stores production performance metrics corresponding to the executing the ML model in the production environment. A production web server in the production environment retrieves the production performance metrics from the data storage in the production environment, and presents the production performance metrics.

Abstract: Systems and methods are provided for a machine learning (ML) pipeline with a unified framework. A training data adapter receives training data in a training data format, processes the training data to match a pipeline data format of a ML pipeline, and transmits reformatted training data to the ML pipeline. A production data adapter receives production data in a production data format, processes the production data to match the pipeline data format, and transmits the reformatted production data to the ML pipeline. The ML pipeline receives and processes the reformatted training data to train a ML model in the ML pipeline, and further receives and processes the reformatted production data using the ML model to generate production artifacts.

Abstract: Systems and methods are provided for a machine learning (ML) pipeline with a unified framework. A ML pipeline trains a ML model in the ML pipeline and in a development environment, and further executes the ML model in a production environment. A development monitoring pipeline, which is in communication with the machine learning pipeline, automatically computes training performance metrics from the training of the ML model in the development environment. A data storage in the development environment stores the training performance metrics. A production monitoring pipeline, which is in communication with the ML pipeline, automatically computes production performance metrics associated with the executing of the ML model in the production environment. A data storage in the production environment stores the production performance metrics.

Abstract: A server having multiple processing units executes a machine-learning application that instantiates a plurality of worker processes that accept connections from client devices at a socket. To prevent excess contention and migration of the worker processes to different processing units, each worker process is specified to a subset of the processing units eligible to execute that respective worker process. The respective subsets for each worker process may be mutually exclusive and each worker process may be assigned a single processing unit eligible to execute that worker process. This enables significantly higher throughput for the worker processes by preventing normal process migration across the plurality of processing units.

Abstract: The disclosed embodiments include modular and configurable computer-implemented processes and systems that facilitate real-time inferencing in distributed computing environments. For example, an apparatus may receive a real-time inferencing request from a computing system, and, based on the inferencing request, the apparatus may obtain configuration data associated with a trained machine-learning process. The apparatus may also perform operations that (i) generate the input dataset based on an application of one or more feature-engineering operations to the elements of payload data in accordance with a first portion of the configuration data, and (ii) generate output data based on the application, in real-time, of the trained machine-learning process to the input dataset. The apparatus may transmit, to the computing system, a response to the real-time inferencing request that includes at least at least a portion of the output data.

Abstract: Various examples are directed to providing a multi-model training and inference pipeline and environment using machine learning for a cloud environment.

Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that facilitate a real-time pre-approval of data exchanges using trained artificial intelligence processes. For example, an apparatus may receive, from a device, application data characterizing an application for an exchange of data involving one or more applicants, and may generate an input dataset based on at least a portion of the application data and on interaction data characterizing the one or more applicants. Further, and based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate, in real-time, elements of output data indicative of a predicted pre-approval of the application for the data exchange involving the one or more applicants, and may transmit the elements of output data to the device for presentation within a digital interface.