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Artificial intelligence (AI) is a branch of science which deals with how intelligence can be implemented. But after implementing, the next important aspect is how data that is generated will be stored and for that we need databases. A database is basically a pool of data which stores data in both sequential and non-sequential format.
What is an AI database?
Essentially, an AI database is a database built with the sole purpose of speeding up Machine Learning (ML) model training. A number of tech companies are already developing dedicated AI chips to alleviate the heavy processing load in new hardware products as vendors roll out more AI-based features that require significant compute power. Artificial intelligence is understood in this context as a collection of methods that enable a computer to solve tasks that would require intelligence if they were solved by human beings.
An AI database combines data warehousing, advanced analytics, and visualizations in an in-memory database. AI databases should be able to simultaneously ingest, explore, analyze, and visualize fast-moving, complex data within milliseconds. The goal is to lower costs, generate new revenue, and integrate ML models so that businesses can make more efficient, data-driven decisions.
How does this work?
For AI technologies to be able to support human beings in future industrial processes, the systems must be trained through learning processes. Such learning processes are based on inputs of appropriate data and the use of system oriented algorithms. Following this, the trained systems are used to supplement human capacities, both the physical as well as the cognitive and decision-making capacities.
It is mainly the cognitive capacities that AI is assisting, in which case it is not only for saving time and money but also for improving quality and for liberating human beings from having to perform simple, repetitive tasks. The idea is that human beings are to have more time for the creative.
By using a purpose-built database you can speed up the machine learning model training. Using an AI database can help you better wrangle volume, velocity, and complex data governance and management challenges that are associated with training machine learning, to save time and optimize resources.
An AI database differentiates from a traditional database on the following fronts, as explained by Radalj (VP and Principal Software Engineer for Kenitica’s Advanced Technology Group);
- Accelerated data ingestion,
- Co-locality of in-memory data (parallel processing across database nodes), and
- A common platform for data scientists, software engineers, and database administrators to iterate and test models faster and apply results directly to analytics.
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Some of the benefits of AI are saving time and money, improving quality, and higher employee satisfaction. You can liberate your employees by having AI take over their small repetitive tasks, which will allow your employees to have more time for the creative planning of intelligent processes. They can put their focus on new technical business models, instead of the same boring task, which will increase their satisfaction.
The bottom line value of an AI database is in optimizing compute and database resources. This lets you create better ML and deep learning models, train them faster and more efficiently, and maintain a through line to how that AI will be applied to your business.
