Machine learning inference calls for database query processing

#The field of machine learning has become widely acknowledged as a likely significant driver of the future of technology. Organizations everywhere now seek to use machine learning techniques to address a wide variety of problems, such as optimizing aspects of their products, processes, customer experience, etc. While the high-level view of machine learning sounds simple—e.g., provide training data to a computer, to allow the computer to automatically learn from the training data to generate a model that can make predictions for other data—implementing machine learning techniques in practice can be tremendously difficult.

#This difficulty is partially due to the underlying algorithmic and mathematical complexities of machine learning algorithms, which are typically developed by academic researchers or individuals at the forefront of the field. Additionally, it is also difficult to generate, update, and deploy useful models, which can be extremely time and resource consumptive and filled with complexities. Moreover, machine learning models tend to be extremely focused on particular use cases and operating environments, and thus any change to the underlying environment or use case may require a complete regeneration of a new model. Further, constructing and deploying machine learning technologies is quite different from traditional software engineering, and requires practices and architectures different from what traditional software engineering development teams are familiar with. While machine learning techniques provide many benefits to organizations, use of such machine learning techniques requires significant specialized knowledge that is not easy to use with traditional data processing using relational databases and other data stores.

#Various embodiments in accordance with the present disclosure will be described with reference to the drawings, in which:

#FIG. 1 is a diagram illustrating an environment for machine learning inference calls for database query processing according to some embodiments.

#FIG. 2 is a diagram illustrating an asynchronous request handler according to some embodiments.

#The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for machine learning inference calls for database query processing According to some embodiments, machine learning inference calls can be integrated into database queries to enable the use of machine learning techniques without requiring specialized machine learning knowledge on the part of users. In some embodiments, machine learning calls can be integrated into database queries, such as structure query language (SQL) queries, or other popular query languages used to process structured data, without requiring significant changes to applications, database services, etc.

#FIG. 1 is a diagram illustrating an environment for machine learning inference calls for database query processing according to some embodiments. As shown in FIG. 1, a provider network 100 can include a database service 112. A user may have structured data which is stored in one or more database instances 110 of database service 112. The data may be added to the database service by the user, from user device 102 or may be added from services of provider network 100 or other services external to provider network 100. This data may be analyzed to obtain useful information for the user. A part of this analysis may include using machine learning techniques to perform inference on the data. For example, text data may be extracted from images stored in database service 112, text data may be analyzed to identify sentiments associated with snippets of the text data, and/or other specialized models may be used to perform inference on the user's data to obtain information about the data. However, as discussed, use of machine learning techniques often requires specialized knowledge and is not well integrated into data management services, such as database service 112. Embodiments address these issues by providing techniques for making machine learning inference calls for database query processing.

#A provider network 100 (or, “cloud” provider network) provides users with the ability to utilize one or more of a variety of types of computing-related resources such as compute resources (e.g., executing virtual machine (VM) instances and/or containers, executing batch jobs, executing code without provisioning servers), data/storage resources (e.g., object storage, block-level storage, data archival storage, databases and database tables, etc.), network-related resources (e.g., configuring virtual networks including groups of compute resources, content delivery networks (CDNs), Domain Name Service (DNS)), application resources (e.g., databases, application build/deployment services), access policies or roles, identity policies or roles, machine images, routers and other data processing resources, etc. These and other computing resources may be provided as services, such as a hardware virtualization service that can execute compute instances, a storage service that can store data objects, etc. The users (or “customers”) of provider networks 100 may utilize one or more user accounts that are associated with a customer account, though these terms may be used somewhat interchangeably depending upon the context of use. Users may interact with a provider network 100 across one or more intermediate networks 106 (e.g., the internet) via one or more interface(s) 104, such as through use of application programming interface (API) calls, via a console implemented as a website or application, etc. The interface(s) 104 may be part of, or serve as a front-end to, a control plane 102 of the provider network 100 that includes “backend” services supporting and enabling the services that may be more directly offered to customers.