ABSTRACT
Abstract
A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of U.S. patent application Ser. No. 17/506,536, entitled AUTOMATED CLOUD DATA AND TECHNOLOGY SOLUTION DELIVERY USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELING, filed on Oct. 20, 2021, and now issued as U.S. Pat. No. 11,416,754, which is hereby incorporated by reference in its entirety.
FIELD OF THE DISCLOSURE
The present disclosure is generally directed to techniques for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling, and more particularly, for training and operating one or more machine learning models to analyze current and future architecture state information and generate infrastructure-as-code.
BACKGROUND
Cloud data and technology solution delivery and transformations are costly affairs and take a long time to execute due to manual design, development, test, and delivery processes that are largely dependent upon expert engineering talent that is challenging to afford, or to acquire for small and large organizations alike. Data curation and data management processes like data accuracy, data cataloging, de-duplication, data security, data anonymization, data governance and suitable architecture delivery processes are prone to various risk factors including human errors due to lack of knowledge and execution, as well as time constraints. Empirical data indicates that 70% of budgets for given migration projects are consumed by data readiness operations. The knowledge required for an efficient technology delivery transformation is distributed across multiple areas and is neither governed nor consolidated and centralized to enable proficient blueprints required for such complex transformations. Data and technology landscape across multi-cloud and hybrid cloud solutions with varied service offerings are highly complex to comprehend. Still further, conventional static visualization techniques that are shared across many organization are inefficient, because among other things, users are not able to apply filters and such visualizations do not update to keep pace with changes in data over time.
Simply put, conventional environmental provisioning techniques are inadequate. Complex delivery problems present in modern deployments (e.g., on premises, multi-cloud, leveraging cloud hosting in open source solutions, etc.) are not fully addressed. Each customer's existing computing environment may include legacy services that must be individually analyzed, resisting any systematic approaches. Further, provisioning and migration strategies provide no guarantees regarding system completeness/validity. Furthermore, current state/architecture must be assessed manually, and provisioning decisions are frozen in time, and not adjusted based on new or changed information. Further, conventional technologies do not leverage or systematize institutional knowledge. Improved techniques that solve existing pain points are needed.
BRIEF SUMMARY
In one aspect, a computer-implemented method for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling includes (i) receiving input current data and architecture state information; (ii) analyzing the input current data and/or architecture state information using a first machine learning model trained by accessing codified knowledge; (iii) receiving input future data and architecture state information; (iv) analyzing the input future data and/or architecture state information using a second machine learning model trained by analyzing one or more historical labeled inputs; and (v) generating infrastructure-as-code corresponding to a future computing environment.
In another aspect, a computing system for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling includes one or more processors; and a memory comprising instructions, that when executed, cause the computing system to: (i) receive input current data and architecture state information; (ii) analyze the input current data and/or architecture state information using a first machine learning model trained by accessing codified knowledge; (iii) receive input future data and architecture state information; (iv) analyze the input future data and/or architecture state information using a second machine learning model trained by analyzing one or more historical labeled inputs; and (v) generate infrastructure-as-code corresponding to a future computing environment.
In yet another aspect, a non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause a computer to: (i) receive input current data and architecture state information; (ii) analyze the input current data and/or architecture state information using a first machine learning model trained by accessing codified knowledge; (iii) receive input future data and architecture state information; (iv) analyze the input future data and/or architecture state information using a second machine learning model trained by analyzing one or more historical labeled inputs; and (v) generate infrastructure-as-code corresponding to a future computing environment.
BRIEF DESCRIPTION OF THE DRAWINGS
The figures described below depict various aspects of the system and methods disclosed therein. It should be understood that each figure depicts one embodiment of a particular aspect of the disclosed system and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Further, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals.
There are shown in the drawings arrangements which are presently discussed, it being understood, however, that the present aspects are not limited to the precise arrangements and instrumentalities shown, wherein:
FIG. 1 depicts an exemplary computing environment in which environmental discovery, environmental validation and automated knowledge engine generation may be performed, in some aspects;
FIG. 2 is an exemplary block flow diagram depicting a computer-implemented method performing environmental discovery, environmental validation and automated knowledge engine generation, according to some aspects;
FIG. 3 is an exemplary block flow diagram depicting a computer-implemented method for performing machine learning training and operation, according to an aspect;
FIG. 4 is an exemplary block flow diagram depicting a computer-implemented method for collecting current architecture state information, validating current information, and generating input templates, according to an aspect;
FIG. 5 is an exemplary block flow diagram depicting a computer-implemented method for analyzing future data and architecture state, collecting future state information, determining objectives and/or intents, generating/displaying previews, validating future state information and generating input templates, according to an aspect;
FIG. 6 A is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data structure engines using machine learning, according to an aspect;
FIG. 6 B is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data quality and regulatory engines using machine learning, according to an aspect;
FIG. 6 C is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data governance engines using machine learning, according to an aspect;
FIG. 6 D is an exemplary block flow diagram depicting a computer-implemented method for generating one or more global data engines using machine learning, according to an aspect;
FIG. 6 E is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data pipeline pattern engines using machine learning, according to an aspect;
FIG. 6 F is an exemplary block flow diagram depicting a computer-implemented method for generating one or more technical module engines using machine learning, according to an aspect;
FIG. 6 G is an exemplary block flow diagram depicting a computer-implemented method for generating one or more pattern knowledge engines using machine learning, according to an aspect;
FIG. 6 H is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data visualization engines using machine learning, according to an aspect;
FIG. 7 is an exemplary block diagram depicting exemplary machine learning and artificial intelligence models, according to an aspect;
FIG. 8 A is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating a descriptive analytics machine learning model, according to one aspect;
FIG. 8 B is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating a predictive analytics machine learning model, according to one aspect;
FIG. 8 C is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating a diagnostic analytics machine learning model, according to one aspect;
FIG. 8 D is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating another diagnostic analytics machine learning model, according to one aspect;
FIG. 8 E is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating a prescriptive analytics machine learning model, according to one aspect;
FIG. 9 is an exemplary block flow diagram depicting a computer-implemented output engine method, according to an aspect;
FIG. 10 is an exemplary block flow diagram depicting a computer-implemented implementation engine method, according to an aspect; and
FIG. 11 A is an exemplary flow diagram depicting a computer-implemented method for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling, according to an aspect.
FIG. 11 B is an exemplary flow diagram depicting a computer-implemented method for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling, according to an aspect.
FIG. 11 C is an exemplary flow diagram depicting a computer-implemented method for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling, according to an aspect.
DETAILED DESCRIPTION
Overview
The aspects described herein relate to, inter alia, machine learning techniques for environmental discovery, environmental validation, and/or automated knowledge engine generation, and more particularly, to training and operating one or more machine learning models to analyze current and future architecture state information and generate infrastructure-as-code.
Specifically, the present techniques include methods and systems for modularizing and codifying processes for performing environmental discovery/scanning, environmental validation, and automated knowledge engine generation using machine learning (ML) and/or artificial intelligence (AI), including those existing processes on premises involving legacy technologies.
The present techniques identify key phases of the migration process, fully assess current state, architecture and building blocks, and determine future state architecture, considering cloud-agnostic and open source targets, taking into account the customer's preferences regarding computing targets and heterogeneous service types. The present techniques may generate knowledge engines using ML, and execute the knowledge engines to determine a turnkey environment and/or step-by-step instructions for the customer, wherein the ML-based recommendations are updated over time (e.g., as new services are released).
The present techniques enable AI and ML-based based decision making for multi-cloud, hybrid cloud and cloud agnostic data and technology deliveries and transformations across Infrastructure-as-a-Service (Iaas), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), etc. The present techniques may enable a warehouse of modularized data and technology building blocks that are continuously updated and improved via cloud-native or cloud agnostic or open source services and packages driving data enablement. The present techniques may also have a central knowledge engine ingesting data from multiple data sources (intellectual property, videos, blogs, news etc.) enabling federation of knowledge at optimal cost. The present techniques may include multiple ML-based knowledge engines that make recommendations on the right blend of on premise, cloud agnostic, and multi-cloud native modules required for efficient and innovative data and tech solution delivery/transformation, accelerating time to market, improving economics and significantly reducing risk through automation.
Exemplary Computing Environment
FIG. 1 depicts a computing environment 100 in which environmental discovery, environmental validation and automated knowledge engine generation may be performed, in accordance with various aspects discussed herein.
In the example aspect of FIG. 1 , computing environment 100 includes client(s) 102 , which may comprise one or more computers. In various aspects, client(s) 102 comprise multiple computers, which may comprise multiple, redundant, or replicated client computers accessed by one or more users. The example aspect of FIG. 1 further includes one or more servers 104 that may include one or more servers. In further aspects, the servers 104 may be implemented as cloud-based servers, such as a cloud-based computing platform. For example,
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of U.S. patent application Ser. No. 17/506,536, entitled AUTOMATED CLOUD DATA AND TECHNOLOGY SOLUTION DELIVERY USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELING, filed on Oct. 20, 2021, and now issued as U.S. Pat. No. 11,416,754, which is hereby incorporated by reference in its entirety.
FIELD OF THE DISCLOSURE
The present disclosure is generally directed to techniques for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling, and more particularly, for training and operating one or more machine learning models to analyze current and future architecture state information and generate infrastructure-as-code.
BACKGROUND
Cloud data and technology solution delivery and transformations are costly affairs and take a long time to execute due to manual design, development, test, and delivery processes that are largely dependent upon expert engineering talent that is challenging to afford, or to acquire for small and large organizations alike. Data curation and data management processes like data accuracy, data cataloging, de-duplication, data security, data anonymization, data governance and suitable architecture delivery processes are prone to various risk factors including human errors due to lack of knowledge and execution, as well as time constraints. Empirical data indicates that 70% of budgets for given migration projects are consumed by data readiness operations. The knowledge required for an efficient technology delivery transformation is distributed across multiple areas and is neither governed nor consolidated and centralized to enable proficient blueprints required for such complex transformations. Data and technology landscape across multi-cloud and hybrid cloud solutions with varied service offerings are highly complex to comprehend. Still further, conventional static visualization techniques that are shared across many organization are inefficient, because among other things, users are not able to apply filters and such visualizations do not update to keep pace with changes in data over time.
Simply put, conventional environmental provisioning techniques are inadequate. Complex delivery problems present in modern deployments (e.g., on premises, multi-cloud, leveraging cloud hosting in open source solutions, etc.) are not fully addressed. Each customer's existing computing environment may include legacy services that must be individually analyzed, resisting any systematic approaches. Further, provisioning and migration strategies provide no guarantees regarding system completeness/validity. Furthermore, current state/architecture must be assessed manually, and provisioning decisions are frozen in time, and not adjusted based on new or changed information. Further, conventional technologies do not leverage or systematize institutional knowledge. Improved techniques that solve existing pain points are needed.
BRIEF SUMMARY
In one aspect, a computer-implemented method for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling includes (i) receiving input current data and architecture state information; (ii) analyzing the input current data and/or architecture state information using a first machine learning model trained by accessing codified knowledge; (iii) receiving input future data and architecture state information; (iv) analyzing the input future data and/or architecture state information using a second machine learning model trained by analyzing one or more historical labeled inputs; and (v) generating infrastructure-as-code corresponding to a future computing environment.
In another aspect, a computing system for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling includes one or more processors; and a memory comprising instructions, that when executed, cause the computing system to: (i) receive input current data and architecture state information; (ii) analyze the input current data and/or architecture state information using a first machine learning model trained by accessing codified knowledge; (iii) receive input future data and architecture state information; (iv) analyze the input future data and/or architecture state information using a second machine learning model trained by analyzing one or more historical labeled inputs; and (v) generate infrastructure-as-code corresponding to a future computing environment.
In yet another aspect, a non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause a computer to: (i) receive input current data and architecture state information; (ii) analyze the input current data and/or architecture state information using a first machine learning model trained by accessing codified knowledge; (iii) receive input future data and architecture state information; (iv) analyze the input future data and/or architecture state information using a second machine learning model trained by analyzing one or more historical labeled inputs; and (v) generate infrastructure-as-code corresponding to a future computing environment.
BRIEF DESCRIPTION OF THE DRAWINGS
The figures described below depict various aspects of the system and methods disclosed therein. It should be understood that each figure depicts one embodiment of a particular aspect of the disclosed system and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Further, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals.
There are shown in the drawings arrangements which are presently discussed, it being understood, however, that the present aspects are not limited to the precise arrangements and instrumentalities shown, wherein:
FIG. 1 depicts an exemplary computing environment in which environmental discovery, environmental validation and automated knowledge engine generation may be performed, in some aspects;
FIG. 2 is an exemplary block flow diagram depicting a computer-implemented method performing environmental discovery, environmental validation and automated knowledge engine generation, according to some aspects;
FIG. 3 is an exemplary block flow diagram depicting a computer-implemented method for performing machine learning training and operation, according to an aspect;
FIG. 4 is an exemplary block flow diagram depicting a computer-implemented method for collecting current architecture state information, validating current information, and generating input templates, according to an aspect;
FIG. 5 is an exemplary block flow diagram depicting a computer-implemented method for analyzing future data and architecture state, collecting future state information, determining objectives and/or intents, generating/displaying previews, validating future state information and generating input templates, according to an aspect;
FIG. 6 A is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data structure engines using machine learning, according to an aspect;
FIG. 6 B is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data quality and regulatory engines using machine learning, according to an aspect;
FIG. 6 C is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data governance engines using machine learning, according to an aspect;
FIG. 6 D is an exemplary block flow diagram depicting a computer-implemented method for generating one or more global data engines using machine learning, according to an aspect;
FIG. 6 E is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data pipeline pattern engines using machine learning, according to an aspect;
FIG. 6 F is an exemplary block flow diagram depicting a computer-implemented method for generating one or more technical module engines using machine learning, according to an aspect;
FIG. 6 G is an exemplary block flow diagram depicting a computer-implemented method for generating one or more pattern knowledge engines using machine learning, according to an aspect;
FIG. 6 H is an exemplary block flow diagram depicting a computer-implemented method for generating one or more data visualization engines using machine learning, according to an aspect;
FIG. 7 is an exemplary block diagram depicting exemplary machine learning and artificial intelligence models, according to an aspect;
FIG. 8 A is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating a descriptive analytics machine learning model, according to one aspect;
FIG. 8 B is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating a predictive analytics machine learning model, according to one aspect;
FIG. 8 C is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating a diagnostic analytics machine learning model, according to one aspect;
FIG. 8 D is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating another diagnostic analytics machine learning model, according to one aspect;
FIG. 8 E is an exemplary block flow diagram depicting a computer-implemented method for training and/or operating a prescriptive analytics machine learning model, according to one aspect;
FIG. 9 is an exemplary block flow diagram depicting a computer-implemented output engine method, according to an aspect;
FIG. 10 is an exemplary block flow diagram depicting a computer-implemented implementation engine method, according to an aspect; and
FIG. 11 A is an exemplary flow diagram depicting a computer-implemented method for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling, according to an aspect.
FIG. 11 B is an exemplary flow diagram depicting a computer-implemented method for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling, according to an aspect.
FIG. 11 C is an exemplary flow diagram depicting a computer-implemented method for automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling, according to an aspect.
DETAILED DESCRIPTION
Overview
The aspects described herein relate to, inter alia, machine learning techniques for environmental discovery, environmental validation, and/or automated knowledge engine generation, and more particularly, to training and operating one or more machine learning models to analyze current and future architecture state information and generate infrastructure-as-code.
Specifically, the present techniques include methods and systems for modularizing and codifying processes for performing environmental discovery/scanning, environmental validation, and automated knowledge engine generation using machine learning (ML) and/or artificial intelligence (AI), including those existing processes on premises involving legacy technologies.
The present techniques identify key phases of the migration process, fully assess current state, architecture and building blocks, and determine future state architecture, considering cloud-agnostic and open source targets, taking into account the customer's preferences regarding computing targets and heterogeneous service types. The present techniques may generate knowledge engines using ML, and execute the knowledge engines to determine a turnkey environment and/or step-by-step instructions for the customer, wherein the ML-based recommendations are updated over time (e.g., as new services are released).
The present techniques enable AI and ML-based based decision making for multi-cloud, hybrid cloud and cloud agnostic data and technology deliveries and transformations across Infrastructure-as-a-Service (Iaas), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), etc. The present techniques may enable a warehouse of modularized data and technology building blocks that are continuously updated and improved via cloud-native or cloud agnostic or open source services and packages driving data enablement. The present techniques may also have a central knowledge engine ingesting data from multiple data sources (intellectual property, videos, blogs, news etc.) enabling federation of knowledge at optimal cost. The present techniques may include multiple ML-based knowledge engines that make recommendations on the right blend of on premise, cloud agnostic, and multi-cloud native modules required for efficient and innovative data and tech solution delivery/transformation, accelerating time to market, improving economics and significantly reducing risk through automation.
Exemplary Computing Environment
FIG. 1 depicts a computing environment 100 in which environmental discovery, environmental validation and automated knowledge engine generation may be performed, in accordance with various aspects discussed herein.
In the example aspect of FIG. 1 , computing environment 100 includes client(s) 102 , which may comprise one or more computers. In various aspects, client(s) 102 comprise multiple computers, which may comprise multiple, redundant, or replicated client computers accessed by one or more users. The example aspect of FIG. 1 further includes one or more servers 104 that may include one or more servers. In further aspects, the servers 104 may be implemented as cloud-based servers, such as a cloud-based computing platform. For example, servers 104 may be any one or more cloud-based platform(s) such as MICROSOFT AZURE, AMAZON AWS, Terraform, etc. The environment 100 may further include a current computing environment 106 , representing a current computing environment (e.g., on premises) of a customer and/or future computing environment 108 , representing a future computing environment (e.g., a cloud computing environment, multi-cloud environment, etc.) of a customer. The environment 100 may further include an electronic network 100 communicatively coupling other aspects of the environment 100 .
As described herein, in some aspects, servers 104 may perform the functionalities as discussed herein as part of a âcloudâ network or may otherwise communicate with other hardware or software components within one or more cloud computing environments to send, retrieve, or otherwise analyze data or information described herein. For example, in aspects of the present techniques, the current computing environment 106 may comprise a customer on-premise computing environment, a multi-cloud computing environment, a public cloud computing environment, a private cloud computing environment, and/or a hybrid cloud computing environment. For example, the customer may host one or more services in a public cloud computing environment (e.g., Alibaba Cloud, Amazon Web Services (AWS), Google Cloud, IBM Cloud, Microsoft Azure, etc.). The public cloud computing environment may be a traditional off-premise cloud (i.e., not physically hosted at a location owned/controlled by the customer). Alternatively, or in addition, aspects of the public cloud may be hosted on-premise at a location owned/controlled by the customer. The public cloud may be partitioned using visualization and multi-tenancy techniques, and may include one or more of the customer's IaaS and/or PaaS services.
In some aspects of the present techniques, the current computing environment 106 of the customer may comprise a private cloud that includes one or more cloud computing resources (e.g., one or more servers, one or more databases, one or more virtual machines, etc.) dedicated to the customer's exclusive use. In some aspects, the private cloud may be distinguished by its isolation to hardware exclusive to the customer's use. The private clouds may be located on-premise of the customer, or constructed from off-premise cloud computing resources (e.g., cloud computing resources located in a remote data center). The private clouds may be third-party managed and/or dedicated clouds.
In still further aspects of the present techniques, the current computing environment 106 may comprise a hybrid cloud that includes multiple cloud computing environments communicatively coupled via one or more networks (e.g., the network 110 ). For example, in a hybrid cloud computing aspect, the current computing environment 106 may include one or more private clouds, one or more public clouds, a bare-metal (e.g., non-cloud based) system, etc. The future computing environment 108 may comprise one or more public clouds, one or more private clouds, one or more bare-metal systems/servers, and/or one or more hybrid clouds. The servers 104 may be implemented as one or more public clouds, one or more private clouds, one or more hybrid clouds, and/or one or more bare-metal systems/servers. For example, the servers 104 may be implemented as a private cloud computing environment that orchestrates the migration of a current computing environment 106 implemented as a first hybrid cloud (e.g., comprising two public clouds and three private clouds) to a future computing environment 108 implemented as a second hybrid cloud (e.g., comprising one public cloud and five private clouds).
The client device 102 may be any suitable device (e.g., a laptop, a smart phone, a tablet, a wearable device, a blade server, etc.). The client device 102 may include a memory and a processor for, respectively, storing and executing one or more modules. The memory may include one or more suitable storage media such as a magnetic storage device, a solid-state drive, random access memory (RAM), etc. A proprietor of migration techniques may access the environment 100 via the client device 102 , to access services or other components of the environment 100 via the network 110 .
The network 110 may comprise any suitable network or networks, including a local area network (LAN), wide area network (WAN), Internet, or combination thereof. For example, the network 106 may include a wireless cellular service (e.g., 4G). Generally, the network 110 enables bidirectional communication between the client device 102 and the servers 104 ; the servers 104 and the current computing environment 106 ; the servers 104 and the future computing environment 108 , etc. As shown in FIG. 1 , servers 104 are communicatively connected, via computer network 110 to the one or more computing environments
106 and 108 via network 110 . In some aspects, network 110 may comprise a cellular base station, such as cell tower(s), communicating to the one or more components of the environment 100 via wired/wireless communications based on any one or more of various mobile phone standards, including NMT, GSM, CDMA, UMMTS, LTE, 5G, or the like. Additionally or alternatively, network 110 may comprise one or more routers, wireless switches, or other such wireless connection points communicating to the components of the environment 100 via wireless communications based on any one or more of various wireless standards, including by non-limiting example, IEEE 802.11a/b/c/g (WIFI), the BLUETOOTH standard, or the like.
The one or more servers 104 may include one or more processors 120 , one or more computer memories 122 , one or more network interface controllers (NICs) 124 and an electronic database 126 . The NIC 124 may include any suitable network interface controller(s), and may communicate over the network 110 via any suitable wired and/or wireless connection. The servers 104 may include one or more input device (not depicted) and may include one or more device for allowing a user to enter inputs (e.g., data) into the servers 104 . For example, the input device may include a keyboard, a mouse, a microphone, a camera, etc. The NIC may include one or more transceivers (e.g., WWAN, WLAN, and/or WPAN transceivers) functioning in accordance with IEEE standards, 3GPP standards, or other standards, and that may be used in receipt and transmission of data via external/network ports connected to computer network 110 .
The database 126 may be a relational database, such as Oracle, DB2, MySQL, a NoSQL based database, such as MongoDB, or another suitable database. The database 126 may store data used to train and/or operate one or more ML/AI models. The database 126 may store runtime data (e.g., a customer response received via the network 110 ). In various aspects, server(s) 104 may be referred to herein as âmigration server(s).â The servers 104 may implement client-server platform technology that may interact, via the computer bus, with the memory(s) 122 (including the applications(s), component(s), API(s), data, etc. stored therein) and/or database 126 to implement or perform the machine readable instructions, methods, processes, elements or limitations, as illustrated, depicted, or described for the various flowcharts, illustrations, diagrams, figures, and/or other disclosure herein.
The processor 120 may include one or more suitable processors (e.g., central processing units (CPUs) and/or graphics processing units (GPUs)). The processor 120 may be connected to the memory 122 via a computer bus (not depicted) responsible for transmitting electronic data, data packets, or otherwise electronic signals to and from the processor 120 and memory 122 in order to implement or perform the machine readable instructions, methods, processes, elements or limitations, as illustrated, depicted, or described for the various flowcharts, illustrations, diagrams, figures, and/or other disclosure herein. The processor 120 may interface with the memory 122 via a computer bus to execute an operating system (OS) and/or computing instructions contained therein, and/or to access other services/aspects. For example, the processor 120 may interface with the memory 122 via the computer bus to create, read, update, delete, or otherwise access or interact with the data stored in memory 122 and/or the database 126 .
The memory 122 may include one or more forms of volatile and/or non-volatile, fixed and/or removable memory, such as read-only memory (ROM), electronic programmable read-only memory (EPROM), random access memory (RAM), erasable electronic programmable read-only memory (EEPROM), and/or other hard drives, flash memory, MicroSD cards, and others. The memory 122 may store an operating system (OS) (e.g., Microsoft Windows, Linux, UNIX, etc.) capable of facilitating the functionalities, apps, methods, or other software as discussed herein.
The memory 122 may store a plurality of computing modules 140 , implemented as respective sets of computer-executable instructions (e.g., one or more source code libraries, trained machine learning models such as neural networks, convolutional neural networks, etc.) as described herein.
In general, a computer program or computer based product, application, or code (e.g., the model(s), such as machine learning models, or other computing instructions described herein) may be stored on a computer usable storage medium, or tangible, non-transitory computer-readable medium (e.g., standard random access memory (RAM), an optical disc, a universal serial bus (USB) drive, or the like) having such computer-readable program code or computer instructions embodied therein, wherein the computer-readable program code or computer instructions may be installed on or otherwise adapted to be executed by the processor(s) 120 (e.g., working in connection with the respective operating system in memory 122 ) to facilitate, implement, or perform the machine readable instructions, methods, processes, elements or limitations, as illustrated, depicted, or described for the various flowcharts, illustrations, diagrams, figures, and/or other disclosure herein. In this regard, the program code may be implemented in any desired program language, and may be implemented as machine code, assembly code, byte code, interpretable source code or the like (e.g., via Golang, Python, C, C++, C #, Objective-C, Java, Scala, ActionScript, JavaScript, HTML, CSS, XML, etc.).
For example, in some aspects, the computing modules 140 may include a ML model training module 142 , comprising a set of computer-executable instructions implementing machine learning training, configuration, parameterization and/or storage functionality. The ML model training module 142 may initialize, train and/or store one or more ML knowledge engines, as discussed herein. The ML knowledge engines, or âenginesâ may be stored in the database 126 , which is accessible or otherwise communicatively coupled to the servers 104 . The modules 140 may store machine readable instructions, including one or more application(s), one or more software component(s), and/or one or more application programming interfaces (APIs), which may be implemented to facilitate or perform the features, functions, or other disclosure described herein, such as any methods, processes, elements or limitations, as illustrated, depicted, or described for the various flowcharts, illustrations, diagrams, figures, and/or other disclosure herein. For example, at least some of the applications, software components, or APIs may be, include, otherwise be part of, an environmental discovery, validation and automatic knowledge generation machine learning model or system.
The ML training module 142 may train one or more ML models (e.g., an artificial neural network). One or more training data sets may be used for model training in the present techniques, as discussed herein. The input data may have a particular shape that may affect the ANN network architecture. The elements of the training data set may comprise tensors scaled to small values (e.g., in the range of (â1.0, 1.0)). In some aspects, a preprocessing layer may be included in training (and operation) which applies principal component analysis (PCA) or another technique to the input data. PCA or another dimensionality reduction technique may be applied during training to reduce dimensionality from a high number to a relatively smaller number. Reducing dimensionality may result in a substantial reduction in computational resources (e.g., memory and CPU cycles) required to train and/or analyze the input data.
In general, training an ANN may include establishing a network architecture, or topology, adding layers including activation functions for each layer (e.g., a âleakyâ rectified linear unit (ReLU), softmax, hyperbolic tangent, etc.), loss function, and optimizer. In an aspect, the ANN may use different activation functions at each layer, or as between hidden layers and the output layer. A suitable optimizer may include Adam and Nadam optimizers. In an aspect, a different neural network type may be chosen (e.g., a recurrent neural network, a deep learning neural network, etc.). Training data may be divided into training, validation, and testing data. For example, 20% of the training data set may be held back for later validation and/or testing. In that example, 80% of the training data set may be used for training. In that example, the training data set data may be shuffled before being so divided. Data input to the artificial neural network may be encoded in an N-dimensional tensor, array, matrix, and/or other suitable data structure. In some aspects, training may be performed by successive evaluation (e.g., looping) of the network, using training labeled training samples. The process of training the ANN may cause weights, or parameters, of the ANN to be created. The weights may be initialized to random values. The weights may be adjusted as the network is successively trained, by using one of several gradient descent algorithms, to reduce loss and to cause the values output by the network to converge to expected, or âlearnedâ, values. In an aspect, a regression may be used which has no activation function. Therein, input data may be normalized by mean centering, and a mean squared error loss function may be used, in addition to mean absolute error, to determine the appropriate loss as well as to quantify the accuracy of the outputs.
The ML training module 142 may receive labeled data at an input layer of a model having a networked layer architecture (e.g., an artificial neural network, a convolutional neural network, etc.) for training the one or more ML models to generate ML models (e.g., the ML model at blocks 624 of FIG. 6 C ). The received data may be propagated through one or more connected deep layers of the ML model to establish weights of one or more nodes, or neurons, of the respective layers. Initially, the weights may be initialized to random values, and one or more suitable activation functions may be chosen for the training process, as will be appreciated by those of ordinary skill in the art. The method may include training a respective output layer of the one or more machine learning models. The output layer may be trained to output a prediction, for example.
The data used to train the ANN may include heterogeneous data (e.g., textual data, image data, audio data, etc.). In some aspects, multiple ANNs may be separately trained and/or operated. In some aspects, the present techniques may include using a machine learning framework (e.g., TensorFlow, Keras, scikit-learn, etc.) to facilitate the training and/or operation of machine learning models.
In various aspects, an ML model, as described herein, may be trained using a supervised or unsupervised machine learning program or algorithm. The machine learning program or algorithm may employ a neural network, which may be a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns in two or more features or feature datasets (e.g., structured data, unstructured data, etc.) in a particular areas of interest. The machine learning programs or algorithms may also include natural language processing, semantic analysis, automatic reasoning, regression analysis, support vector machine (SVM) analysis, decision tree analysis, random forest analysis, K-Nearest neighbor analysis, naïve Bayes analysis, clustering, reinforcement learning, and/or other machine learning algorithms and/or techniques. In some aspects, the artificial intelligence and/or machine learning based algorithms may be included as a library or package executed on server(s) 104 . For example, libraries may include the TensorFlow based library, the Pytorch library, and/or the scikit-learn Python library.
Machine learning may involve identifying and recognizing patterns in existing data (such as data risk issues, data quality issues, sensitive data, etc.) in order to facilitate making predictions, classifications, and/or identifications for subsequent data (such as using the models to determine or generate a classification or prediction for, or associated with, applying a data governance engine to train a descriptive analytics model).
Machine learning model(s), may be created and trained based upon example data (e.g., âtraining dataâ) inputs or data (which may be termed âfeaturesâ and âlabelsâ) in order to make valid and reliable predictions for new inputs, such as testing level or production level data or inputs. In supervised machine learning, a machine learning program operating on a server, computing device, or otherwise processor(s), may be provided with example inputs (e.g., âfeaturesâ) and their associated, or observed, outputs (e.g., âlabelsâ) in order for the machine learning program or algorithm to determine or discover rules, relationships, patterns, or otherwise machine learning âmodelsâ that map such inputs (e.g., âfeaturesâ) to the outputs (e.g., labels), for example, by determining and/or assigning weights or other metrics to the model across its various feature categories. Such rules, relationships, or otherwise models may then be provided subsequent inputs in order for the model, executing on the server, computing device, or otherwise processor(s), to predict, based on the discovered rules, relationships, or model, an expected output.
In unsupervised machine learning, the server, computing device, or otherwise processor(s), may be required to find its own structure in unlabeled example inputs, where, for example multiple training iterations are executed by the server, computing device, or otherwise processor(s) to train multiple generations of models until a satisfactory model, e.g., a model that provides sufficient prediction accuracy when given test level or production level data or inputs, is generated.
Supervised learning and/or unsupervised machine learning may also comprise retraining, relearning, or otherwise updating models with new, or different, information, which may include information received, ingested, generated, or otherwise used over time. The disclosures herein may use one or both of such supervised or unsupervised machine learning techniques.
In various aspects, training the ML models herein may include generating an ensemble model comprising multiple models or sub-models, comprising models trained by the same and/or different AI algorithms, as described herein, and that are configured to operate together. For example, in some aspects, each model may be trained to identify or predict diagnostic analytics, where each model may output or determine a classification for a computing environment such that a given environment may be identified, assigned, determined, or classified with one or more environment classifications.
In some aspects, the computing modules 140 may include a machine learning operation module 144 , comprising a set of computer-executable instructions implementing machine learning loading, configuration, initialization and/or operation functionality. The ML operation module 144 may include instructions for storing trained models (e.g., in the electronic database 126 , as a pickled binary, etc.). Once trained, the one or more trained ML models may be operated in inference mode, whereupon when provided with de novo input that the model has not previously been provided, the model may output one or more predictions, classifications, etc. as described herein.
The architecture of the ML model training module 142 and the ML operation module 144 as separate modules represent advantageous improvements over the prior art. In conventional computing systems that include multiple machine learning algorithms, for performing various functions, the models are often added to each individual module or set of instructions independent from other algorithms/modules. This is wasteful of storage resources, resulting in significant code duplication. Further, repeating ML model storage in this way may result in retraining of the same model aspects in multiple places, wasting computational resources. By consolidating ML model training and ML model operation into two respective modules that may be reused by any of the various ML algorithms/ modeling suites of the present techniques, waste of storage and computation is avoided. Further, this organization enables training jobs to be organized by a task scheduling module (not depicted), for efficiently allocating computing resources for training and operation, to avoid overloading the underlying system hardware, and to enable training to be performed using distributed computing resources (e.g., via the network 110 ) and/or using parallel computing strategies.
In some aspects, the computing modules 140 may include an input/output (I/O) module 146 , comprising a set of computer-executable instructions implementing communication functions. The I/ O module 146 may include a communication component configured to communicate (e.g., send and receive) data via one or more external/network port(s) to one or more networks or local terminals, such as computer network 110 and/or the client 102 (for rendering or visualizing) described herein. In some aspects, servers 104 may include a client-server platform technology such as ASP.NET, Java J2EE, Ruby on Rails, Node.js, a web service or online API, responsive for receiving and responding to electronic requests.
I/ O module 146 may further include or implement an operator interface configured to present information to an administrator or operator and/or receive inputs from the administrator and/or operator. An operator interface may provide a display screen (e.g., via the terminal 109 ). I/ O module 146 may facilitate I/O components (e.g., ports, capacitive or resistive touch sensitive input panels, keys, buttons, lights, LEDs), which may be directly accessible via, or attached to, servers 104 or may be indirectly accessible via or attached to the client device 102 . According to some aspects, an administrator or operator may access the servers 104 via the client device 102 to review information, make changes, input training data, initiate training via the ML training module 142 , and/or perform other functions (e.g., operation of one or more trained models via the ML operation module 144 ).
In some aspects, the computing modules 140 may include a natural language processing (NLP) module 148 , comprising a set of computer-executable instructions implementing natural language processing functionality.
In some aspects, the computing modules 140 may include a validation module 150 , comprising a set of computer-executable instructions implementing environmental discovery and/or environmental validation, functionality. The validation module 150 may include a set of computer-implemented functionality (e.g., one or more scripts) that determine the acceleration and readiness of an existing computing system (e.g., the current computing environment 106 ). For example, the validation module 150 may analyze the memory footprint of an operating system executing in the current computing environment 106 , such as the services executing therein. For example, the validation module 150 may collect the amount of memory consumed, version of software, etc. The validation module 150 may include a set of instructions for training one or more machine learning model to evaluate input (e.g., an electronic template form describing a future computing environment) for validity, by analyzing one or more historical labeled inputs (e.g., a plurality of electronic template forms labeled as valid/invalid). The validation module 150 may access codified knowledge for training the one or more ML model. For example, the proprietor of the present techniques may prepare a codified data set that includes disconnected components (e.g., a <figure-callout id="100" label="component" filenames="US11645548-
CLAIMS
Claims ( 20 )
What is claimed:
1. A computer-implemented method for improving codification of institutional knowledge using machine learning and artificial intelligence modeling, comprising:
receiving input current data and architecture state information;
analyzing the input current data and/or architecture state information using a first machine learning model trained by accessing codified knowledge;
receiving input future data and architecture state information;
analyzing the input future data and/or architecture state information using a second machine learning model trained by analyzing one or more historical labeled inputs; and
generating infrastructure-as-code corresponding to a future computing environment.
2. The computer-implemented method of claim 1 , further comprising:
training the first machine learning model to analyze first data and architecture state input information corresponding to the current computing environment;
training the second machine learning model to analyze second data and architecture state input information corresponding to the future computing environment; and
analyzing the input future data using a knowledge engine to generate extracted information.
3. The computer-implemented method of claim 2 , wherein the knowledge engine is selected from the group consisting of a data structure engine, a data quality and remediation engine, a data governance engine, a global data/enterprise engine, a data pipeline pattern engine, a technical modules engine, a pattern knowledge engine; or a data visualization engine.
4. The computer-implemented method of claim 1 , further comprising:
generating an input template electronic form including the input future data and architecture state information.
5. The computer-implemented method of claim 1 , further comprising:
processing one or more user response using natural language processing to determine one or more desired characteristic of the future computing environment.
6. The computer-implemented method of claim 1 , further comprising:
generating a number of deployment options using a trained machine learning model, wherein the deployment options include at least one of a one-click deployment or a manual delivery deployment.
7. The computer-implemented method of claim 6 , further comprising:
displaying the deployment options to a user.
8. A computing system, comprising:
one or more processors; and
a memory having stored thereon instructions, that when executed, cause the computing system to:
receive input current data and architecture state information;
analyze the input current data and/or architecture state information using a first machine learning model trained by accessing codified knowledge;
receive input future data and architecture state information;
analyze the input future data and/or architecture state information using a second machine learning model trained by analyzing one or more historical labeled inputs; and
generate infrastructure-as-code corresponding to a future computing environment.
9. The computing system of claim 8 , the memory having stored thereon further instructions that, when executed, cause the system to:
train the first machine learning model to analyze first data and architecture state input information corresponding to the current computing environment;
train the second machine learning model to analyze second data and architecture state input information corresponding to the future computing environment; and
analyze the input future data using a knowledge engine to generate extracted information.
10. The computing system of claim 9 , the memory having stored thereon further instructions wherein the knowledge engine is selected from the group consisting of:
a data structure engine, a data quality and remediation engine, a data governance engine, a global data/enterprise engine, a data pipeline pattern engine, a technical modules engine, a pattern knowledge engine; or a data visualization engine.
11. The computing system of claim 8 , the memory having stored thereon further instructions that, when executed, cause the system to:
generate an input template electronic form including the input future data and architecture state information.
12. The computing system of claim 8 , the memory having stored thereon further instructions that, when executed, cause the system to:
process one or more user response using natural language processing to determine one or more desired characteristic of the future computing environment.
13. The computing system of claim 8 , the memory having stored thereon further instructions that, when executed, cause the system to:
generate a number of deployment options using a trained machine learning model wherein the deployment options include at least one of a one-click deployment or a manual delivery deployment.
14. The computing system of claim 13 , the memory having stored thereon further instructions that, when executed, cause the system to:
display the deployment options to a user.
15. A non-transitory computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause a computer to:
receive input current data and architecture state information;
analyze the input current data and/or architecture state information using a first machine learning model trained by accessing codified knowledge;
receive input future data and architecture state information;
analyze the input future data and/or architecture state information using a second machine learning model trained by analyzing one or more historical labeled inputs; and
generate infrastructure-as-code corresponding to a future computing environment.
16. The non-transitory computer-readable storage medium of claim 15 , having stored thereon further executable instructions that, when executed, cause a computer to:
train the first machine learning model to analyze first data and architecture state input information corresponding to the current computing environment;
train the second machine learning model to analyze second data and architecture state input information corresponding to the future computing environment; and
analyze the input future data using a knowledge engine to generate extracted information.
17. The non-transitory computer-readable storage medium of claim 15 , having stored thereon further executable instructions that, when executed, cause a computer to:
generate an input template electronic form including the input future data and architecture state information.
18. The non-transitory computer-readable storage medium of claim 15 , having stored thereon further executable instructions that, when executed, cause a computer to:
process one or more user response using natural language processing to determine one or more desired characteristic of the future computing environment.
19. The non-transitory computer-readable storage medium of claim 15 , having stored thereon further executable instructions that, when executed, cause a computer to:
generate a number of deployment options using a trained machine learning model wherein the deployment options include at least one of a one-click deployment or a manual delivery deployment.
20. The non-transitory computer-readable storage medium of claim 19 , having stored thereon further executable instructions that, when executed, cause a computer to:
display the deployment options to a user.
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