What are databases?
Databases are organized collections of structured data stored electronically in computer systems. They enable efficient data management, retrieval, and manipulation. For decades, traditional databases have been used to store and manage data for various applications, such as online transaction processing (OLTP), online analytical processing (OLAP), and big data.
However, with the rise of artificial intelligence (AI) and machine learning (ML), a growing need exists for databases that can efficiently handle the massive amounts of data required to train AI models.
How do AI and database technology work together?
AI databases are purpose-built to optimize the AI learning and training process. They help organizations manage the volume, velocity, and complex data governance challenges of training ML and deep learning models.
Some key aspects of how AI and database technology work together include:
- Accelerated data ingestion: AI databases can quickly ingest and process large volumes of data, including real-time streaming data, enabling businesses to take fast action on AI-driven insights
- Parallel processing: AI databases support parallel processing across database nodes, mimicking the human brain's ability to process multiple stimuli. This allows for faster query processing and model training
- Unified model training: AI databases provide a common platform for data scientists, software engineers, and database administrators to collaborate, iterate, and test models faster, applying results directly to analytics
Examples of AI databases in practice include Microsoft Batch AI for training deep learning and ML models on Azure GPUs, Google's AutoML for automating ML model design, and Kinetica's GPU-accelerated database for fast data ingestion and analytics.
Why does your business need an AI database?
- Faster model training: AI databases can significantly reduce the time required to train ML and deep learning models by optimizing compute and database resources
- Improved efficiency: By streamlining data management and processing, AI databases enable data scientists and engineers to focus on developing and refining AI models rather than dealing with infrastructure challenges
- Real-time insights: AI databases can process massive streams of real-time data, allowing businesses to make data-driven decisions and take action quickly
- Competitive advantage: Organizations that effectively leverage AI databases can create better ML and deep learning models, giving them an edge over competitors in their industry
