Databases are technology-driven, and it is normal that as technology evolves and advances, it impacts the functioning of databases. The crux of database management systems is to provide the right data at the right time. The task has now become much easier by using Artificial Intelligence or AI in database management. In the earlier days, databases remained confined to the premises or devices, or systems, but today, the cloud is the most preferred location for storing data.
The technology of AI flourishes on its predictive abilities and plays a critical role in better database management. It has made the task of database administrators easier who can now slice and dice data in many more ways that were not possible earlier, explains the database administrators of RemoteDBA.com, a company the provides end-to-end service in database management.
The AI database
Artificial Intelligence is a package of several methods that help a computer solve tasks by using intelligence like any human being. Databases that use AI technology are known as AI databases, although the basic database structure and its functionalities remain. The ease of operations has gone up many times, and the speedier systems can provide the most precise and complete information. Machine Learning (ML) model training implanted within the AI databases gathers more speed with AI support. The dedicated AI chips used in the databases make light of the heavy workload that databases have to handle as these databases have more AI-based features that require considerable power of computing.
An AI database is a conglomeration of data warehousing and analytics supported by visualizations in an in-memory database. AI databases have the exceptional ability to gather, analyze, explore and visualize fast-moving and complex data in a fraction of a second. AI databases can lower costs and integrate ML models to enable businesses to make faster decisions based on accurate data.
The added features of AI databases
AI databases provide several value-added features that set it apart and put it miles ahead of traditional databases. A typical example of one such advanced feature in AI databases is the capability of full-text search and text analytics which is a deviation from the way traditional databases have query-based operations by considering keywords, phrases, and a combination of both with the help of Boolean operators. Another distinctive feature of AI databases is that it accelerates the usually expensive Machine Learning (ML) models as the latter remains immersed within the database. This feature is especially useful for machine learning applications as it saves them time and hard work to look for an appropriate Machine Learning model.
Adapting the DataOps methodology
Empowered by AI databases, organizations are now moving toward adapting the DataOps methodology. DataOps is a set of practices that helps automate the development and testing of data analytics pipelines besides facilitating communication across the data science teams. Increasing the efficiency and agility of the data science teams is the goal of DataOps. Over and above, DataOps provides a framework for effective interactions between developers, data scientists, architects, and database administrators. To reduce the cycle time of data analytics operations, you can integrate the framework with DevOps that is now quite common in IT operations.
Some of the world’s biggest companies are extensively using AI and ML. Here are some examples that should help better understand the expanded scope of AI databases over traditional databases.
Walmart – For the smooth running of the stores across all locations, Walmart uses Machine Learning and AI that helps in image processing to establish a channel of communication and information delivery. With the help of thousands of video cameras, sensors placed strategically on shelves, and other technologies, Walmart stores employees can know when stock levels of certain products are depleting or the stored items are about to start rotting. In one instance, the images alerted the store employees about bananas turning brown without inspecting the fruit. Traffic flow systems help stores employees identify the lean times when footfalls are low so that they can start gathering the shopping carts tucked away in the parking lots or restock the shelves.
Visual data and image processing are vital tools for increasing efficiencies in physical workspaces.
Apple– Apple has been a pioneer in using AI, and it keeps packing more AI-driven features in its products, smartphones, tablets, and PCs. Apple uses AI to aid its efforts in personalization to web search as well as Siri results. Users can now conduct searches on Apple devices by using images instead of keywords that return more precise and accurate results and increase the possibilities of purchasing the products. AI-powered photo enhancement capabilities of iPhones have taken smartphone photography to incredible heights.
The use of AI for Siri helps the virtual assistant to indulge in self-learning based on customer communications to improve its capabilities.
Exxon Mobil – The oil and gas giant uses AI-powered algorithms to makes it easier for drilling at the bottom of the sea by its team of explorers. The system receives its training from the datasets generated from previous jobs and information gathered from ocean floor surveys. Empowered by the Drilling Advisory System, the exploration team was able to optimize the drilling parameters automatically. It resulted in lowering cost, enhancing safety performance, and enhanced drilling. AI system is most appropriate for taking care of repetitive works in any organization. The AI system makes use of lessons learned from previous activities to chart the way to the future.
Amazon – Amazon is perhaps the leading company that started deploying AI much early and is still a trendsetter in using the technology for improving performance and efficiencies at all levels besides lowering costs and increasing profits. The entire operation of Amazon runs on AI, and every annual business plan makes room for including newer systems.
The recommendation engine of Amazon that keeps offering all the probable items of interest to its customers is the most glaring example of using AI that triggers the urge to purchase something.