Businesses are increasingly harnessing machine learning (ML) to tackle various challenges, ranging from sales forecasting to process automation. However, the success of artificial intelligence (AI) relies heavily on the availability of quality data; without it, the algorithms and methods that drive AI cannot function effectively. The greater the volume of quality data accessible for analysis, the more sophisticated and accurate the models that can be developed.
Anna Fenyushina, a lead architect at VK Tech specializing in data services, explores the different generations of ML, the types of data necessary for their implementation, and how modern data storage solutions can enhance AI development.
AI is often perceived as a form of magic at the consumer level. To demystify this perception, it is essential to understand the mathematics and technology behind AI applications. Users engage with various applications—including chatbots, business systems, and analytical tools—that often rely on multiple ML models combined with additional logic to create a seamless experience.
In deploying these models, businesses have a choice: they can either utilize pre-trained services hosted by cloud providers or develop their own models, which requires gathering training data and allocating computational resources for both model training and deployment.
The software applications users interact with merely serve as a façade, while the core consists of trained ML models. The effectiveness of these models hinges on the underlying mathematical and technical foundations.
There are several stages and implementations of AI models, including classic ML, neural networks, and large language models (LLM).
Classic ML relies on algorithms rooted in higher mathematics and probability theory to address various tasks such as classification, regression, and clustering. For instance, in predicting binary outcomes, the goal of ML is to determine the parameters that optimally separate data points in a multidimensional space. These models can handle business tasks like sales forecasting, credit scoring, customer segmentation, and more, typically requiring relatively small datasets and limited computational resources.
Neural networks, on the other hand, mimic human brain functions to learn and recognize patterns. These systems can handle more complex applications, including computer vision and natural language processing. As neural networks have evolved, they allow for working with larger datasets and more intricate data types, necessitating increased storage and computational capabilities.
LLMs are specialized neural networks trained on vast amounts of data to understand and generate human language. Models like ChatGPT and Claude are examples of LLMs that can be employed across various business scenarios, including customer service and content generation. However, developing proprietary LLMs is feasible only for organizations with unique datasets, significant budgets, skilled ML teams, and robust IT infrastructure.
For many companies, the most effective strategy involves adapting existing open-source models or leveraging APIs from established providers, which can significantly reduce costs and time associated with building models from scratch.
The ongoing evolution of AI technologies underscores the importance of data storage and platforms in supporting the development of sophisticated models. As businesses continue to embrace these advancements, the competitive landscape is likely to shift dramatically, favoring those who can effectively leverage quality data and innovative AI solutions.
Informational material. 18+.
