Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, information regarding desired services may be obtained. The information may be used to identify an architecture usable to provide the services. The architecture may be used to select hardware components to support the architecture. The hardware components and architecture may be used to establish a system able to provide the desired services.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, components of an artificial intelligence workflow pipeline may be evaluated for migration or replication. If positively evaluated, then alternative locations may be identified based on resources needed to operate the components or reduced cost versions of the components. The alternative locations and other locations for obtaining reduced cost versions of the components may be evaluated and one of each location may be selected and used to update operation of the artificial intelligence workflow pipeline.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, components of an artificial intelligence workflow pipeline may be evaluated for migration or replication. If positively evaluated, then alternative locations may be identified based on resources needed to operate the components or reduced cost versions of the components. The alternative locations may be evaluated and one may be selected and used to update operation of the artificial intelligence workflow pipeline.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, components of an artificial intelligence workflow pipeline may be evaluated for migration, replication, or tuning. If positively evaluated for tuning, then a model tuning process may be performed. Locations for the tuning process may be evaluated based on resource consumption estimates to select a tuning location and deployment location.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, components of an artificial intelligence workflow pipeline may be evaluated for migration, replication, or tuning. If positively evaluated for tuning, then a model tuning process may be performed. Locations for the tuning process may be evaluated based on resource consumption estimates to select a tuning location and deployment location. Resource use limits for portions of the process may be enforced during location selection.

Abstract: Methods and systems for managing operation of a request processing pipeline are provided. To service requests, the request processing pipeline may accept unstructured text based requests. The unstructured text may be used to obtain prompts for evaluation by a trained generative machine learning model. The pairs may be evaluated to rank order them with respect to one another. A best ranked one of the pair may be used to service the request. The request may be serviced by providing the response of the best ranked one of the pairs, by initiating provisioning of computer implemented services using the response, and/or via other processes.

Abstract: Methods and systems for managing operation of a request processing pipeline are provided. To service requests, the request processing pipeline may accept unstructured text based requests. The unstructured text may be used to obtain prompts for evaluation by a trained generative machine learning model. The pairs may be evaluated to rank order them with respect to one another. A best ranked one of the pair may be used to service the request. The request may be serviced by providing the response of the best ranked one of the pairs, by initiating provisioning of computer implemented services using the response, and/or via other processes.

Abstract: Methods and systems for managing operation of a request processing pipeline are provided. To service requests, the request processing pipeline may accept unstructured text based requests. The unstructured text may be used to obtain prompts for evaluation by a trained generative machine learning model. The pairs may be evaluated to rank order them with respect to one another. A best ranked one of the pair may be used to service the request. The request may be serviced by providing the response of the best ranked one of the pairs, by initiating provisioning of computer implemented services using the response, and/or via other processes.

Abstract: Methods and systems for managing operation of a request processing pipeline are provided. To service requests, the request processing pipeline may accept unstructured text based requests. The unstructured text may be used to obtain prompts for evaluation by a trained generative machine learning model. The pairs may be evaluated to rank order them with respect to one another. A best ranked one of the pair may be used to service the request. The request may be serviced by providing the response of the best ranked one of the pairs, by initiating provisioning of computer implemented services using the response, and/or via other processes.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, operation of an artificial intelligence workflow pipeline may be monitored. If any components of the artificial intelligence workflow pipeline are identified as exhibiting unexpected behavior based on the monitoring, additional analysis may be performed to attempt to identify issues impacting the components. Remediations may then be performed to attempt to address the issues impacting the components.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, components of an artificial intelligence workflow pipeline may be evaluated for migration, replication, or tuning. If a new model instance is to be instantiated based on the evaluation, a selection process may be performed to identify a host location. The selection process may take into account resource costs for modifying the model for compatibility and useful life estimated for the new instance.

Abstract: Methods and systems for managing operation of a request processing pipeline are provided. To service requests, the request processing pipeline may accept unstructured text based requests. The unstructured text may be used to obtain prompts for evaluation by a trained generative machine learning model. The pairs may be evaluated to rank order them with respect to one another. A best ranked one of the pair may be used to service the request. The request may be serviced by providing the response of the best ranked one of the pairs, by initiating provisioning of computer implemented services using the response, and/or via other processes.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, components of an artificial intelligence workflow pipeline may be evaluated for migration or replication. If positively evaluated, then alternative locations may be identified based on resources needed to operate the components or reduced cost versions of the components. The alternative locations and other locations for obtaining reduced cost versions of the components may be evaluated at least based on data transmission resource cost, and one of each location may be selected and used to update operation of the artificial intelligence workflow pipeline.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, varying performance expectations may be used as a basis for identifying desirable from undesirable operation. The varying performance expectations may take into account the impacts of both time and changes in pipelines used by the system to provide desired services. By proactively taking into account potential changes in the pipeline, the operation of the pipeline may be less likely to be flagged as undesirable when the system is not actually operating in an undesirable manner.

Abstract: Methods, systems, and devices for providing for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, hardware components may be bundled into hardware bundles. The hardware bundles may be used to satisfy subscriptions for the services, and limit use of the hardware bundles when subscription limits are reached. The hardware bundles may include direct management hardware components and indirect management hardware components. Limits on use of the hardware bundles may be enforced by analyzing workloads performed by the hardware bundles.

Abstract: Methods and systems for managing service deployment are disclosed. To deploy services, dependencies and other characteristics (such as context data, capability data, placement rules, and workflow data) of services and the infrastructure boundaries in which the services are running may be dynamically collected, analyzed and updated. By dynamically analyzing dependencies and other characteristics (such as context data, capability data, placement rules, and workflow data) of services and the infrastructure boundaries, efficiency of use of computing resources may be improved by recycling existing workflow; statically defined operations and hard-coded conditional logic of services may also be avoided each time an environment in which the services are running has changed.

Abstract: Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, hardware components may be bundled into hardware bundles. The hardware bundles may be used to satisfy subscriptions for the services, and limit use of the hardware bundles when subscription limits are reached. The hardware bundles may include direct management hardware components and indirect management hardware components. The performance of the hardware bundles may be characterized to facilitate future selection for use of the hardware bundles in various processes. Overtime, the performance of the hardware bundles may be checked.

Abstract: A method for managing VDI environments includes: obtaining a first resource related parameter (FRRP) for a first resource (FR) and a second resource related parameter (SRRP) for a second resource (SR) from a database; assembling a set of API combinations to generate a set of VDI environments; testing, for a per user average resource utilization value (PUARUV) of the FR and SR, each VDI environment across a range of users based on the FRRP and SRRP; providing the range of users and set of VDI environments to an analyzer, in which the analyzer is instructed to generate a model that ranks the set of VDI environments to obtain a VDI environment that provides the lowest PUARUV; generating a trained model by training the model using the range of users, set of VDI environments, PUARUVs, FRRP, and SRRP; and initiating notification of an administrator about the trained model.

Abstract: A method for managing a virtual desktop infrastructure (VDI) environment includes: obtaining a plurality of target resource specific pool specific configuration templates for a target resource, in which each of the plurality of target resource specific pool specific configuration templates is associated with one or a plurality of virtual desktop (VD) pools, in which the target resource is storage; obtaining a common configuration template set; generating a VD pool configuration for each of the plurality of VD pools using the plurality of target resource specific pool specific configuration templates and the common configuration template set to obtain a plurality of VD pool configurations; selecting a default VD pool from the plurality of VD pools; and deploying, based on the selection, a plurality of VDs into the default VD pool using a VD pool configuration associated with the default VD pool.

Abstract: A method for managing a virtual desktop infrastructure (VDI) environment includes: obtaining a plurality of target resource specific pool specific configuration templates for a target resource, in which each of the plurality of target resource specific pool specific configuration templates is associated with one or a plurality of virtual desktop (VD) pools, in which the target resource is memory; obtaining a common configuration template set; generating a VD pool configuration for each of the plurality of VD pools using the plurality of target resource specific pool specific configuration templates and the common configuration template set to obtain a plurality of VD pool configurations; selecting a default VD pool from the plurality of VD pools; and deploying, based on the selection, a plurality of VDs into the default VD pool using a VD pool configuration associated with the default VD pool.