KR102626108B1 - User authentication method on ai platforms - Google Patents

Abstract

According to the present disclosure, a method for authenticating a user in an artificial intelligence platform is performed. Specifically, according to the present disclosure, a computing device receives a user's API request, stores the user's authentication information in external storage, and generates information for executing a process corresponding to the API request received from the user. And, information for executing the process and access path information to the external storage are transmitted to the model learning device, and the authentication information corresponds to information that is continuously updated based on information related to the state of the artificial intelligence platform. do.

Description

How to authenticate users on artificial intelligence platforms {USER AUTHENTICATION METHOD ON AI PLATFORMS}

The present disclosure relates to a method of authenticating a user in an artificial intelligence platform, and specifically to a method of storing the user's authentication information in external storage and continuously updating the authentication information based on information related to the state of the artificial intelligence platform. It's about.

In general, there are two paths in how a computer program handles a user's request: either the process that receives the user's request directly processes the task, or a separate process is created to handle the request. When creating a new process to process a request, the process is executed the moment the request is made and terminates after the task is completed, so user authentication occurs only at the moment the process is created.

However, in the case of development platforms that deal with artificial intelligence, the environment that processes user requests and the environment in which artificial intelligence model learning actually takes place are often run on separate hardware or separated by tasks such as virtualization, and users It is often designed to not terminate and wait for the next training request until you explicitly terminate the process (e.g. the process that trains the model).

In these cases, the authentication information that an authorized user passed when executing a process often was no longer valid when the process was terminated, causing the problem that the process had to be restarted or created again.

Not only at the end of the learning process, but also when distributing the artificial intelligence model to the user after the learning is completed, if the authentication information delivered when executing the process is different from the current authentication information of the user who is to receive the artificial intelligence model, the distribution may fail. There was a problem.

Therefore, after creating a process related to learning of an artificial intelligence model on an artificial intelligence platform, when the process is terminated or distributed to the user, the user's authentication information is used without the user having to perform additional authentication or restart or re-create the process. There is a demand in the art for a method of doing so.

Korean registered patent KR 1773233 discloses a method for handling repeated situations where biometric authentication is not recognized.

This disclosure was developed in response to the above-described background technology. When receiving a user's API request, the user's authentication information is stored in an external storage, and while creating a process corresponding to the request, the access path information to the external storage is included. The purpose is to make user authentication more convenient on the artificial intelligence platform by providing and continuously updating authentication information stored in external storage.

Meanwhile, the technical problem to be achieved by the present disclosure is not limited to the technical problems mentioned above, and may include various technical problems within the scope of what is apparent to those skilled in the art from the contents described below.

According to an embodiment of the present disclosure for realizing the above-described task, a method for continuous deployment of an artificial intelligence model by an artificial intelligence platform, performed by a computing device, is disclosed. The method includes executing a process related to learning of an artificial intelligence model requested by a user, and after completing a process related to learning of the artificial intelligence model, distributing the artificial intelligence model to the user based on the user's authentication information. The user's authentication information may be stored in an external storage, and may be information that is continuously updated based on the user's login status or changes in specifications of the artificial intelligence platform.

In one embodiment, after completing the process related to learning of the artificial intelligence model, distributing the artificial intelligence model to the user based on the user's authentication information includes: distributing the user's authentication information stored in the external storage. acquiring; It may include performing validation on the user's authentication information and distributing the artificial intelligence model to the user based on the validation result.

According to an embodiment of the present disclosure for realizing the above-described problem, a method of performing user authentication on an artificial intelligence platform by a computing device is disclosed. The method includes receiving a user's API (Application Programming Interface) request; storing the user's authentication information in external storage; Generating information for executing a process corresponding to an API request received from the user and transmitting information for executing the process and access path information to the external storage to a model learning device, Authentication information may be continuously updated based on information related to the status of the artificial intelligence platform.

In one embodiment, the process corresponding to the API request received from the user is,

It may include processes related to learning of artificial intelligence models.

In one embodiment, the information related to the state of the artificial intelligence platform includes information related to the user's login status; Alternatively, it may include at least one of information related to a change in specifications of the artificial intelligence platform.

In one embodiment, the user's authentication information includes: receiving a login request from the user; It may be updated by generating new authentication information based on the login request and replacing authentication information stored in the external storage with the new authentication information.

In one embodiment, the user's authentication information may not be deleted and may remain in external storage when the user logs out.

In one embodiment, the method includes receiving an API request and first authentication information from the model learning device; It may further include performing validation on the first authentication information and performing a task corresponding to the API request received from the model learning device based on the validation result.

In one embodiment, a task corresponding to an API request received from the model learning device may include a task related to storing learning results of an artificial intelligence model.

In one embodiment, the method includes, when the first authentication information is expired authentication information of the user, generating new authentication information of the user and replacing the authentication information stored in the external storage with the new authentication information. Additional steps may be included.

In one embodiment, the method may further include transmitting a response related to the update to the model training device.

According to an embodiment of the present disclosure for realizing the above-described task, a method performed by a computing device operating in conjunction with an artificial intelligence platform is disclosed. The method includes receiving access path information for an external storage where the user's authentication information is stored and information about a process to be created from the artificial intelligence platform; Creating a process related to artificial intelligence model development based on the information about the process to be created; Upon completion of the process related to developing the artificial intelligence model, obtaining the user's authentication information from the external storage based on the access path information and transmitting the API request and the user's authentication information to the artificial intelligence platform. It can be included.

In one embodiment, the step of transmitting an API request and the user's authentication information to the artificial intelligence platform includes: If authentication by the authentication information fails, authentication information updated from the external storage based on the access path information It may further include re-obtaining and transmitting the API request and the updated authentication information to the artificial intelligence platform.

In one embodiment, the API request may correspond to a task related to storing learning results of an artificial intelligence model.

According to an embodiment of the present disclosure for realizing the above-described problem, a computer program stored in a computer-readable storage medium that allows a computing device to perform an operation related to user authentication on an artificial intelligence platform is disclosed. The operations include: receiving a user's API request; Saving the user's authentication information in external storage; An operation of generating information for executing a process corresponding to an API request received from the user and transmitting information for executing the process and access path information to the external storage to a model learning device, Authentication information may be continuously updated based on information related to the status of the artificial intelligence platform.

According to an embodiment of the present disclosure for realizing the above-described problem, a computing device for performing operations related to user authentication on an artificial intelligence platform is disclosed. The computing device includes a processor including one or more cores and a memory, wherein the processor receives an API request from a user, stores the user's authentication information in external storage, and responds to the API request received from the user. Generate information for executing a process, and transmit information for executing the process and access path information to the external storage to a model learning device, and the authentication information is information related to the state of the artificial intelligence platform. It can be continuously updated based on.

With this disclosure, even if the user's login information changes, tasks related to the artificial intelligence model are performed on the artificial intelligence platform without the need to perform additional authentication tasks or restart or regenerate the process. In particular, in the present disclosure, tasks related to an artificial intelligence model are performed on an artificial intelligence platform by storing the user's authentication information in external storage and continuously updating the authentication information while transmitting only access path information to the external storage to the model learning device. The effect occurs.

1 is a block diagram of a computing device for performing user authentication in an artificial intelligence platform according to an embodiment of the present disclosure.
Figure 2 is a flowchart showing the process of performing user authentication in an artificial intelligence platform according to an embodiment of the present disclosure.
Figure 3 is a conceptual diagram showing a process in which a user's authentication information is stored in an external storage in an artificial intelligence platform according to an embodiment of the present disclosure, and a model learning device acquires the user's authentication information and performs authentication.
FIG. 4 is a conceptual diagram illustrating a process of performing authentication by re-obtaining updated authentication information from an external storage when authentication using authentication information obtained by a model learning device according to an embodiment of the present disclosure fails.
Figure 5 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

This disclosure discloses a method for storing a user's authentication information in an external storage, creating a process corresponding to a request, providing access path information to the external storage, and continuously updating the authentication information stored in the external storage. .

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the disclosure. However, it is clear that these embodiments may be practiced without these specific descriptions.

As used herein, the terms “component,” “module,” “system,” and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an implementation of software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. In the present disclosure, one or more components may be distributed between two or more computers, or execution environments. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. Components can transmit signals, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet). Depending on the data being transmitted, they may communicate through local and/or remote processes.

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” should be interpreted to mean “when it contains only A,” “when it contains only B,” or “when it is a combination of A and B.”

Those skilled in the art will additionally understand that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or both. It should be recognized that it can be implemented with combinations of. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable anyone skilled in the art to use or practice the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present disclosure is not limited to the embodiments presented herein. This disclosure is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

In this disclosure, the artificial intelligence platform may refer to a device or system that receives requests related to artificial intelligence development from users who access the platform with the main purpose of developing artificial intelligence and allows other connected devices to perform tasks. .

In the present disclosure, a model learning device may refer to hardware such as a GPU or a virtualization environment where learning of an artificial intelligence model is actually performed. The artificial intelligence platform does not directly perform model learning at the request of the user, but rather allows a separately built model learning device designed to efficiently perform artificial intelligence model learning to perform or stop learning the artificial intelligence model. Can act as an intermediary.

1 is a block diagram of a computing device for performing user authentication in an artificial intelligence platform according to an embodiment of the present disclosure.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include different configurations for performing the computing environment of the computing device 100, and only some of the disclosed configurations may configure the computing device 100.

The computing device 100 may include a processor 110, a memory 130, and a network unit 150.

The processor 110 may be composed of one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit) may include a processor for data analysis and deep learning. The processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for learning neural networks, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. Calculations can be performed.

At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, CPU and GPGPU can work together to process learning of network functions and data classification using network functions. Additionally, in one embodiment of the present disclosure, the processors of a plurality of computing devices can be used together to process learning of network functions and data classification using network functions. Additionally, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

The processor 110 of the artificial intelligence platform can receive an API (Application Programming Interface) request from the user. When receiving the API request, the processor 110 generates information for executing a process corresponding to the API request received from the user in the model learning device, and stores the user's authentication information in external storage. In the present disclosure, the external storage is hardware-separated and may exist in a separate space from the hardware on which the artificial intelligence platform operates, or may exist in a separate virtualization space.

In the present disclosure, a process corresponding to an API request received from a user may include a process related to learning of an artificial intelligence model.

In the present disclosure, the user's authentication information may be continuously updated based on information related to the state of the artificial intelligence platform. For example, when a user logs in newly, the user's authentication information is newly created by the processor 110 based on the most recent login, and the authentication information stored in external storage can be replaced with the newly created authentication information. .

As another example, when the specifications of the artificial intelligence platform are changed, specifically, when functions of the artificial intelligence platform are added, the processor 110 may newly generate user authentication information by reflecting the most recent functions. You can. Afterwards, the authentication information stored in the external storage may be replaced with the newly created authentication information.

Additionally, the processor 110 may store the user's authentication information in external storage and generate access path information for the external storage. At this time, the access path information for the external storage does not include the user's authentication information, but only includes information for accessing the storage that stores the user's authentication information. In this case, a separate external storage may be used to store authentication information for each user using the artificial intelligence platform.

The processor 110 may transmit information for executing a process corresponding to an API request and an access path to external storage to the model learning device. In particular, in this case, the processor 110 does not directly transmit the user's authentication information to the model learning device.

When the user newly logs in to the artificial intelligence platform, the processor 110 may generate new authentication information for the user based on the new log-in request. In this case, the processor 110 can identify the external storage where the existing authentication information is stored and replace the existing authentication information with new authentication information.

Alternatively, the processor 110 may not perform the task of deleting the user's authentication information when the user logs out of the artificial intelligence platform. In the present disclosure, the user's authentication information can be stored in external storage even if the user logs out, and a model learning device, etc. can later re-acquire the authentication information remaining in the external storage.

The processor 110 may receive an authentication information update request from the model learning device, generate new authentication information for the current user, and update the authentication information by replacing the authentication information stored in external storage with the new authentication information.

The processor 110 may receive an API request and first authentication information from the model learning device. In this case, the API request received from the model learning device may be a request corresponding to a task related to storing model learning results. Also, in this case, the first authentication information may include authentication information for a valid user, may include authentication information for an invalid user, or may include authentication information for a valid user but has expired authentication information. . Accordingly, the processor 110 may perform validation on the received first authentication information.

If the first authentication information received by the processor 110 from the model learning device is invalid, the processor 110 may reject the API request received from the model learning device. If the processor 110 rejects the API request because the first authentication information received from the model learning device is invalid, the model learning device can reread the authentication information from the external storage and make another API request. The specific process is This will be described later with reference to FIG. 4 .

If the first authentication information received by the processor 110 from the model learning device is valid, the processor 110 may perform a task corresponding to the API request received from the model learning device. For example, the processor 110 may perform the task of storing a model that has completed training in the model learning device according to an API request received from the model learning device. Additionally, the processor 110 may store the model on which learning has been completed and then transmit a response indicating that it has been successfully stored back to the model learning device.

If the first authentication information transmitted from the model learning device is expired authentication information of a valid user, the processor 110 generates new authentication information based on the current login status of the valid user, and uses the authentication information stored in the external storage as new authentication information. It can be replaced with authentication information. In this case as well, since the API request of the model learning device was rejected, the model learning device can read the authentication information again from the external storage and send the API request to the artificial intelligence platform again. When transmitting an API request again, the authentication information acquired by the model learning device and transmitted to the artificial intelligence platform may be the latest authentication information newly generated by the processor 110 of the artificial intelligence platform and stored in external storage. Accordingly, the processor 110 may determine that the corresponding authentication information is valid and perform a task corresponding to an API request of the same content from the model learning device.

If the user's authentication information is transmitted directly as in the past, if there is a long time interval between process execution and completion, such as learning of an artificial intelligence model, the authentication information at the time of process execution and the authentication information at the end of the process are different. There may be cases where termination fails or the process needs to be restarted or created. However, as in the present disclosure, if the user's authentication information is separately stored in external storage and the model learning device does not directly receive user authentication information but only access path information to the external storage, the model learning device sends the API request to artificial intelligence. When sending to the platform, you can always obtain the latest authentication information by reading the updated authentication information stored in external storage. Therefore, the artificial intelligence platform performs tasks corresponding to the API requests of the model learning device without user intervention, resulting in a dramatic increase in convenience for users in using the artificial intelligence platform.

In one embodiment of the present disclosure, the processor 110 may continuously distribute an artificial intelligence model that has been trained by a model learning device based on user authentication information separately stored in external storage.

Specifically, the processor 110 may utilize a model learning device to execute a process related to learning of an artificial intelligence model requested by a user. After completing the process related to learning of the artificial intelligence model, the processor 110 obtains the user's authentication information stored in external storage, performs validation on the user's authentication information, and models the artificial intelligence model based on the validation result. can be distributed to the above users.

When distributing an artificial intelligence model using the method of the present disclosure, the latest authentication information can always be obtained by reading updated authentication information stored in external storage, similar to the process of terminating the process related to learning of the artificial intelligence model. Therefore, in the artificial intelligence platform of the present disclosure, even when the time interval between process start and process end is long, an artificial intelligence model that has completed learning based on the latest authentication information can be automatically distributed to users without user intervention. As a result, the convenience of the process for users to develop and use artificial intelligence models using the artificial intelligence platform is dramatically increased.

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, hard disk type, multimedia card micro type, or card type memory (e.g. (e.g. SD or -Only Memory), and may include at least one type of storage medium among magnetic memory, magnetic disk, and optical disk. The computing device 100 may operate in connection with web storage that performs a storage function of the memory 130 on the Internet. The description of the memory described above is merely an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure includes Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL ( A variety of wired communication systems can be used, such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN).

In addition, the network unit 150 presented in this specification includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), and SC-FDMA ( A variety of wireless communication systems can be used, such as Single Carrier-FDMA) and other systems.

In the present disclosure, the network unit 150 may use any type of wired or wireless communication system.

The techniques described herein can be used in the networks mentioned above, as well as other networks.

Figure 2 is a flowchart showing the process of performing user authentication in an artificial intelligence platform according to an embodiment of the present disclosure.

According to Figure 2, the process of performing user authentication in the artificial intelligence platform of the present disclosure includes receiving the user's API request (S210), storing the user's authentication information in external storage (S220), and receiving the user's authentication information in external storage (S220). It may include generating information for executing a process corresponding to an API request (S230) and transmitting information for executing the process and access path information to an external storage to the model learning device (S240).

In step S210, the processor 110 may receive the user's API request. As described above, in this case, the API request sent by the user may be a request related to learning of an artificial intelligence model, such as 'start learning of an artificial intelligence model'.

In step S220, the processor 110 may store the user's authentication information in external storage. When a user makes a request, such as starting training of an artificial intelligence model, the processor 110 may perform user authentication to check whether the user has valid permissions. When user authentication is completed, the processor 110 may store the user's authentication information in external storage without deleting the user authentication information while performing a task corresponding to the user's API request.

In step S230, the processor 110 may generate information for executing a process corresponding to the API request received from the user. The processor 110 may not create a process to immediately process the API request received from the user, but may have another device perform a process corresponding to the API request. For example, if the API request received from the user is a request related to learning of an artificial intelligence model, the processor 110 generates information for executing a process related to learning of an artificial intelligence model, and uses this information to By transmitting to a separate entity from the intelligence platform, the model learning device can execute processes related to learning of the artificial intelligence model.

In step S240, the processor 110 may transmit information for executing the process and access path information to the external storage to the model learning device. After receiving the user's API request and performing user authentication, the processor 110 may not immediately transmit the user authentication information to the model learning device. Instead, the processor 110 may store the authentication information in external storage, then generate only access path information for the external storage and transmit it to the model learning device.

Unlike the present disclosure, if the processor 110 directly transmits the user's authentication information to the model learning device, the following problem occurs. After the learning process of the artificial intelligence model in the model learning device is completed, the learned model must be saved to the artificial intelligence platform. At this time, the model learning device transmits the user's authentication information and API request (request for storing learning results) received from the artificial intelligence platform to the artificial intelligence platform. However, in fields such as artificial intelligence model learning, there is often a significant time gap between the start of model learning and the saving of the learned model, and in this case, the user's authentication information sent by the artificial intelligence platform may become outdated over time. Depending on the situation, it may become invalid for various reasons. Therefore, when the model learning device transmits an API request along with the user's authentication information to the artificial intelligence platform, user authentication fails due to invalid authentication information, and storage of the model fails.

However, in one embodiment of the present disclosure, the processor 110 of the artificial intelligence platform stores the user's authentication information in external storage, and the model learning device stores the user's authentication information in the external storage at the time of sending the API request to the artificial intelligence platform. obtains. At this time, the authentication information obtained from the external storage may not be authentication information received from the artificial intelligence platform at the start of the learning process, but may be authentication information continuously updated by the artificial intelligence platform, etc. Therefore, the authentication information is likely to be valid authentication information, and tasks such as saving an artificial intelligence model can proceed smoothly without the user performing additional authentication.

Figure 3 is a conceptual diagram showing a process in which a user's authentication information is stored in an external storage in an artificial intelligence platform according to an embodiment of the present disclosure, and a model learning device acquires the user's authentication information and performs authentication.

The artificial intelligence platform 320 may receive an API request from the user 310. Afterwards, the artificial intelligence platform 320 may store the authentication information of the user 310 in the external storage 340. The artificial intelligence platform 320 may transmit information for executing a process corresponding to a request received from the user 310 and an access path to the external storage 340 to the model learning device 330.

The model learning device 330 is a computing device that operates in conjunction with the artificial intelligence platform 320, and can receive access path information to external storage and information about the process to be created from the artificial intelligence platform 320. Thereafter, the model learning device 330 may generate a process related to artificial intelligence model development, for example, a process such as artificial intelligence model learning, based on information about the process to be created.

When execution of the process is completed, for example, when model learning is completed, the model learning device 330 may attempt to save the learned model to the artificial intelligence platform 320. In order to store the learned model, the model learning device 330 may obtain the user's authentication information stored in the external storage 340 at the time of attempting to save the model in the external storage 340 based on access path information. At this time, the authentication information may correspond to information that is continuously updated by events such as the user's login situation or changes in the specifications of the artificial intelligence platform. Afterwards, the model learning device 330 may transmit an API request for storing the artificial intelligence model and the obtained user authentication information to the artificial intelligence platform 320.

The artificial intelligence platform 320 may receive an API request and authentication information, that is, first authentication information, from the model learning device 330 and then perform validation on the first authentication information. Based on the validation result, the artificial intelligence platform 320 can perform a task corresponding to the API request received from the model learning device 330. For example, if the first authentication information is the user's valid authentication information, the artificial intelligence platform 320 may perform a task related to storing the learning results of the artificial intelligence model corresponding to the API request. In contrast, if the first authentication information is invalid authentication information, the artificial intelligence platform 320 may send a response indicating that authentication has failed to the model learning device 330. An embodiment of a case where the authentication information transmitted by the model learning device 330 is invalid will be described later with reference to FIG. 4 .

FIG. 4 is a conceptual diagram illustrating a process of performing authentication by re-obtaining updated authentication information from an external storage when authentication using authentication information obtained by a model learning device according to an embodiment of the present disclosure fails.

As in one embodiment of the present disclosure, the artificial intelligence platform 420 receives an API request from the user 410 as depicted in FIG. 3, and provides information and access path information for creating a process in the model learning device 430. can be transmitted. Afterwards, an API request and user authentication information can be received from the model learning device 430, such as storing an artificial intelligence model, and tasks can be performed accordingly.

If the authentication information received from the model learning device 430, that is, the first authentication information, is invalid, the artificial intelligence platform 420 may transmit information that authentication has failed to the model learning device 430.

In this case, the model learning device 430 may re-acquire the user's updated authentication information from the external storage 440 and transmit the API request and updated authentication information to the artificial intelligence platform 420 again. According to the conventional method, if the user's authentication information is invalid, a problem arises where the process must be restarted or regenerated through user intervention, etc. However, as in the present disclosure, the updated authentication information file is obtained from the external storage 440. In this case, tasks such as saving the learning results of the model can be easily performed. This method can be applied not only when the received authentication information is invalid but also when a failure occurs in the artificial intelligence platform 420.

If the authentication information received from the model learning device 430, that is, the first authentication information, is the user's expired authentication information, the artificial intelligence platform 420 may generate new authentication information based on the current user's situation. Afterwards, the artificial intelligence platform 420 may replace the authentication information stored in the external storage with new authentication information. Thereafter, as described above, a process in which the model learning device 430 re-acquires the authentication information stored in the external storage 440 may be performed. Since the user's authentication information has been updated by the artificial intelligence platform 420, a task corresponding to the API request of the model learning device 430 can be performed on the artificial intelligence platform 420.

Meanwhile, a computer-readable medium storing a data structure is disclosed according to an embodiment of the present disclosure.

Data structure can refer to the organization, management, and storage of data to enable efficient access and modification of data. Data structure can refer to the organization of data to solve a specific problem (e.g., retrieving data, storing data, or modifying data in the shortest possible time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. Logical relationships between data elements may include connection relationships between user-defined data elements. Physical relationships between data elements may include actual relationships between data elements that are physically stored in a computer-readable storage medium (e.g., a persistent storage device). A data structure may specifically include a set of data, relationships between data, and functions or instructions applicable to the data. Effectively designed data structures allow computing devices to perform computations while minimizing the use of the computing device's resources. Specifically, computing devices can increase the efficiency of operations, reading, insertion, deletion, comparison, exchange, and search through effectively designed data structures.

Data structures can be divided into linear data structures and non-linear data structures depending on the type of data structure. A linear data structure may be a structure in which only one piece of data is connected to another piece of data. Linear data structures may include List, Stack, Queue, and Deque. A list can refer to a set of data that has an internal order. The list may include a linked list. A linked list may be a data structure in which data is connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer may contain connection information to the next or previous data. Depending on its form, a linked list can be expressed as a singly linked list, a doubly linked list, or a circularly linked list. A stack may be a data listing structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (for example, inserted or deleted) at only one end of the data structure. Data stored in the stack may have a data structure (LIFO-Last in First Out) where the later it enters, the sooner it comes out. A queue is a data listing structure that allows limited access to data. Unlike the stack, it can be a data structure (FIFO-First in First Out) where data stored later is released later. A deck can be a data structure that can process data at both ends of the data structure.

A non-linear data structure may be a structure in which multiple pieces of data are connected behind one piece of data. Nonlinear data structures may include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. Graph data structure may include a tree data structure. A tree data structure may be a data structure in which there is only one path connecting two different vertices among a plurality of vertices included in the tree. In other words, it may be a data structure that does not form a loop in the graph data structure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. Below, it is described in a unified manner as a neural network. Data structures may include neural networks. And the data structure including the neural network may be stored in a computer-readable medium. Data structures including neural networks also include data preprocessed for processing by a neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may include a loss function for learning. A data structure containing a neural network may include any of the components disclosed above. In other words, the data structure including the neural network includes preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may be configured to include all or any combination of the loss function for learning. In addition to the configurations described above, a data structure containing a neural network may include any other information that determines the characteristics of the neural network. Additionally, the data structure may include all types of data used or generated in the computational process of a neural network and is not limited to the above. Computer-readable media may include computer-readable recording media and/or computer-readable transmission media. A neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node.

The data structure may include data input to the neural network. A data structure containing data input to a neural network may be stored in a computer-readable medium. Data input to the neural network may include learning data input during the neural network learning process and/or input data input to the neural network on which training has been completed. Data input to the neural network may include data that has undergone pre-processing and/or data subject to pre-processing. Preprocessing may include a data processing process to input data into a neural network. Therefore, the data structure may include data subject to preprocessing and data generated by preprocessing. The above-described data structure is only an example and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network. (In this specification, weights and parameters may be used with the same meaning.) And the data structure including the weights of the neural network may be stored in a computer-readable medium. A neural network may include multiple weights. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. Based on the weight, the data value output from the output node can be determined. The above-described data structure is only an example and the present disclosure is not limited thereto.

As an example and not a limitation, the weights may include weights that are changed during the neural network learning process and/or weights for which neural network learning has been completed. Weights that change during the neural network learning process may include weights that change at the start of the learning cycle and/or weights that change during the learning cycle. Weights for which neural network training has been completed may include weights for which a learning cycle has been completed. Therefore, the data structure including the weights of the neural network may include weights that are changed during the neural network learning process and/or the data structure including the weights for which neural network learning has been completed. Therefore, the above-mentioned weights and/or combinations of each weight are included in the data structure including the weights of the neural network. The above-described data structure is only an example and the present disclosure is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or a different computing device and later reorganized and used. Computing devices can transmit and receive data over a network by serializing data structures. Data structures containing the weights of a serialized neural network can be reconstructed on the same computing device or on a different computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure to increase computational efficiency while minimizing the use of computing device resources (e.g., in non-linear data structures, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree) may be included. The foregoing is merely an example and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of a neural network. And the data structure including the hyperparameters of the neural network can be stored in a computer-readable medium. A hyperparameter may be a variable that can be changed by the user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle repetitions, weight initialization (e.g., setting the range of weight values subject to weight initialization), Hidden Unit. It may include a number (e.g., number of hidden layers, number of nodes in hidden layers). The above-described data structure is only an example and the present disclosure is not limited thereto.

Figure 5 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Although the disclosure has been described above as being generally capable of being implemented by a computing device, those skilled in the art will understand that the disclosure can be implemented in combination with computer-executable instructions and/or other program modules that can be executed on one or more computers and/or hardware. It will be well known that it can be implemented as a combination of software.

Typically, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Additionally, those skilled in the art will understand that the methods of the present disclosure can be used on single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. It will be appreciated that other computer system configurations may be implemented, including, but not limited to, each of which may operate in connection with one or more associated devices.

The described embodiments of the disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Computer-readable media can be any medium that can be accessed by a computer, and such computer-readable media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Includes media. Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.

A computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed to encode information within the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 is shown that implements various aspects of the present disclosure, including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106, to processing unit 1104. Processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1104.

System bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, and EEPROM, and is a basic input/output system that helps transfer information between components within the computer 1102, such as during startup. Contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

Computer 1102 may also include an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes - a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM for reading the disk 1122 or reading from or writing to other high-capacity optical media such as DVDs). Hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to system bus 1108 by hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to. The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 1102, drive and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those of ordinary skill in the art will also recognize zip drives, magnetic cassettes, flash memory cards, and cartridges. It will be appreciated that other types of computer-readable media may also be used in the example operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure. .

A number of program modules may be stored in drives and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented on various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as mouse 1140. Other input devices (not shown) may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc. These and other input devices are connected to the processing unit 1104 through an input device interface 1142, which is often connected to the system bus 1108, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also connected to system bus 1108 through an interface, such as a video adapter 1146. In addition to monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, etc.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and is generally connected to computer 1102. For simplicity, only memory storage device 1150 is shown, although it includes many or all of the components described. The logical connections depicted include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154. These LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.

When used in a LAN networking environment, computer 1102 is connected to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed thereon for communicating with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158 or be connected to a communicating computing device on the WAN 1154 or to establish communications over the WAN 1154, such as over the Internet. Have other means. Modem 1158, which may be internal or external and a wired or wireless device, is coupled to system bus 1108 via serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored in remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and that other means of establishing a communications link between computers may be used.

Computer 1102 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) allows connection to the Internet, etc. without wires. Wi-Fi is a wireless technology, like cell phones, that allows these devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a base station. Wi-Fi networks use wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, the Internet, and wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band). .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols and chips that may be referenced in the above description include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. It can be expressed by particles or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be used in electronic hardware, (for convenience) It will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art of this disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash. Includes, but is not limited to, memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Thus, the present disclosure is not limited to the embodiments presented herein but is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

Claims (24)

A method for continuous deployment of an artificial intelligence model by an artificial intelligence platform, performed by a computing device,
Executing a process related to learning of an artificial intelligence model requested by a user; and
After completing a process related to learning of the artificial intelligence model, distributing the artificial intelligence model to the user based on the user's authentication information;
Including,
The user's authentication information is stored in external storage and is continuously updated based on the user's login status or changes in specifications of the artificial intelligence platform,
The steps involved in executing the process involved in training the model are:
Generating information for executing a process corresponding to an API request received from the user; and
transmitting information for executing the process and access path information to the external storage to a model learning device;
Including,
method.
According to claim 1,
After completing the process related to learning of the artificial intelligence model, distributing the artificial intelligence model to the user based on the user's authentication information is:
Obtaining the user's authentication information stored in the external storage;
performing validation on the user's authentication information; and
Distributing the artificial intelligence model to the user based on the validation result;
Including,
method.
A method performed by a computing device to perform user authentication on an artificial intelligence platform, comprising:
Receiving a user's API (Application Programming Interface) request;
storing the user's authentication information in external storage;
Generating information for executing a process corresponding to an API request received from the user; and
transmitting information for executing the process and access path information to the external storage to a model learning device;
Including,
The authentication information is,
Continuously updated based on information related to the status of the artificial intelligence platform,
method.
According to claim 3,
The process corresponding to the API request received from the user is,
Processes involved in learning artificial intelligence models;
Including,
method.
According to claim 3,
Information related to the status of the artificial intelligence platform,
Information related to the user's login status; or
Information related to changes in specifications of the artificial intelligence platform;
Containing at least one of
method.
According to claim 3,
The user's authentication information is,
Receiving a login request from the user;
generating new authentication information based on the login request; and
replacing authentication information stored in the external storage with the new authentication information;
updated by,
method.
According to claim 3,
The user's authentication information is,
When the user logs out, it is not deleted and remains in external storage,
method.
According to claim 3,
Receiving an API request and first authentication information from the model learning device;
performing validation on the first authentication information; and
performing a task corresponding to an API request received from the model learning device based on the validation result;
Containing more,
method.
According to claim 8,
The task corresponding to the API request received from the model learning device is,
Work related to storing learning results of artificial intelligence models;
Including,
method.
According to claim 8,
If the first authentication information is expired authentication information of the user, generating new authentication information of the user; and
replacing authentication information stored in the external storage with the new authentication information;
Containing more,
method.
According to claim 10,
transmitting a response related to the update to the model learning device;
Containing more,
method.
A method performed by a computing device operating in conjunction with an artificial intelligence platform,
Receiving access path information to an external storage where the user's authentication information is stored and information about a process to be created from the artificial intelligence platform;
Creating a process related to artificial intelligence model development based on the information about the process to be created;
Upon completion of a process related to developing the artificial intelligence model, obtaining authentication information of the user from the external storage based on the access path information; and
Transmitting an API request and the user's authentication information to the artificial intelligence platform;
Including,
method.
According to claim 12,
The steps of sending an API request and the user's authentication information to the artificial intelligence platform are:
If authentication using the authentication information fails, re-obtaining updated authentication information from the external storage based on the access path information; and
Transmitting the API request and the updated authentication information to the artificial intelligence platform;
Containing more,
method.
According to claim 12,
The API request is,
Corresponds to tasks related to storing learning results of artificial intelligence models;
method.
A computer program stored on a computer-readable storage medium that causes a computing device to perform operations related to user authentication on an artificial intelligence platform, said operations comprising:
Receiving a user's API request;
Saving the user's authentication information in external storage;
An operation of generating information for executing a process corresponding to an API request received from the user; and
Transmitting information for executing the process and access path information to the external storage to a model learning device;
Including,
The authentication information is,
Continuously updated based on information related to the status of the artificial intelligence platform,
A computer program stored on a computer-readable storage medium.
According to claim 15,
Receiving an API request and first authentication information from the model learning device;
An operation of performing validation on the first authentication information; and
An operation of performing a task corresponding to an API request received from the model learning device based on the validation result;
Containing more,
A computer program stored on a computer-readable storage medium.
According to claim 16,
The task corresponding to the API request received from the model learning device is,
Work related to storing learning results of artificial intelligence models;
Including,
A computer program stored on a computer-readable storage medium.
According to claim 16,
If the first authentication information is expired authentication information of the user, generating new authentication information of the user; and
An operation of replacing authentication information stored in the external storage with the new authentication information;
Containing more,
A computer program stored on a computer-readable storage medium.
According to claim 18,
Transmitting a response related to the update to the model learning device;
Containing more,
A computer program stored on a computer-readable storage medium.
As a computing device,
a processor containing one or more cores;
Memory;
Including,
The processor,
Receiving the user's API request,
Store the user's authentication information in external storage,
Generate information to execute a process corresponding to the API request received from the user, and
Transmitting information for executing the process and access path information to the external storage to a model learning device,
The authentication information is,
Continuously updated based on information related to the status of the artificial intelligence platform,
Computing device.
According to claim 20,
Receiving an API request and first authentication information from the model learning device;
performing validation on the first authentication information; and
performing a task corresponding to an API request received from the model learning device based on the validation result;
To do more,
Computing device.
According to claim 21,
The task corresponding to the API request received from the model learning device is,
Work related to storing learning results of artificial intelligence models;
Including,
Computing device.
According to claim 21,
If the first authentication information is expired authentication information of the user, generating new authentication information of the user; and
replacing authentication information stored in the external storage with the new authentication information;
To do more,
Computing device.
According to claim 23,
transmitting a response related to the update to the model learning device;
To do more,
Computing device.

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