The Role of Large Language Models (LLMs) in Machine Learning
Large language models (LLMs) have become one of the most important and popular tools in natural language processing which falls under AI and overlaps with ML. LLMs enable computers to understand and generate text similar to how humans communicate. They’re currently employed in a variety of consumer and business applications, including sentiment analysis, content generation, language translation, and chatbots. One of the most exciting applications of this technology is in data science.
In this article, we’ll unpack the role of LLMs in machine learning and explore how data scientists use this technology to work more quickly and efficiently. We’ll conclude with an in-depth look at how data scientists pair the Snowflake ecosystem's unique capabilities with LLMs to improve data search and discovery.
The Role of LLM in Machine Learning and AI
Because large-scale data sets have become more widely available and compute power is increasingly scalable and affordable, large language models have gained widespread usage. LLMs play a vital role in making human–computer interactions more natural and effective.
What is an LLM in AI?
A large language model is an artificial intelligence system designed to work with human language. These algorithms consist of an artificial neural network that contains millions to billions of different parameters. Designed to learn like humans, large language models are trained on enormous amounts of textual data gathered from books, articles, internet content, and more. The result is an AI model that can predict, generate, translate, and summarize text with human-like accuracy.
Generative AI and LLMs
Generative AI is a type of artificial intelligence capable of creating original content, including text, audio, video, images, and computer code. Large language models are a subset of generative AI that focuses on generating text content.
The importance of LLM in Natural Language Processing (NLP)
Large language models are essential to natural language processing. They possess an extensive understanding of general language patterns and knowledge based on massive data sets. This enables them to achieve superior results on various tasks, such as question answering, information retrieval, sentiment analysis, and more.
How LLMs Are Used in Machine Learning for Data Science
Large language models help machines develop a deeper understanding of human language and its context. Here are five ways LLMs are used in machine learning for data science.
Topic modeling
Topic modeling is an unstructured machine learning technique that detects clusters of related words and phrases within unstructured text, such as emails, customer service responses, and social media posts. Using topic modeling, data scientists can help organizations identify relevant themes to improve processes. For example, an analysis of customer complaints may reveal themes that indicate a quality control issue with a certain product or shortcomings in customer support processes.
Text classification
Text classification is a structured ML practice that uses text classifiers to label documents based on their content. Large language models assist in automating the categorization of text documents into organized groups. Text classification is integral to numerous ML-powered processes, including sentiment analysis, document analysis, spam detection, and language translation.
Data cleansing and imputation
Preparing data for analysis can be tedious and time-consuming. Large language models can automate many data cleansing tasks, including flagging duplicate data, data parsing and standardization, and identifying anomalies or outliers.
Data labeling
Large language models can be useful in data annotation and labeling tasks. They can propose labels or tags for text data, reducing the manual effort required for annotation. This assistance speeds up the labeling process and allows data scientists to focus on more complex tasks.
Automating data science workflows
Large language models can be used to automate a variety of data science tasks. One example is text summarization. With their ability to quickly analyze and summarize large volumes of textual data, large language models can generate concise summaries of long texts such as podcast transcripts. These summaries can then be analyzed to quickly identify key points and observe patterns and trends. By automating time-consuming processes, large language models free data scientists to focus on deeper analysis and improved decision-making.
Snowflake for LLM-Enabled Machine Learning Applications
The Snowflake Data Cloud is designed to support and advance machine learning initiatives. As the pace of innovation quickens, Snowflake spearheads support for the next generation of AI-powered technologies.
Access all training data in a single location
Machine learning models require massive amounts of data for training and deployment. When relevant data is spread across numerous source systems, looking for and requesting access to data significantly slows development. Snowflake provides a single point of access to a global network of trusted data. With Snowflake, you can bring nearly all data types into your model without complex pipelines and enjoy native support for structured, semi-structured (JSON, Avro, ORC, Parquet, or XML), and unstructured data.
Build LLM-powered data apps
Data scientists no longer need to be tethered to a front-end developer to build intuitive, easy-to-use data apps. Using Streamlit, a pure-Python open-source application framework, data scientists can quickly and easily create beautiful, intuitive data applications. With Streamlit, Snowflake users can use LLMs to build apps with integrations to web-hosted LLM APIs using external functions and Streamlit as an interactive front end for LLM-powered apps.
Aggregate and analyze unstructured data
Unstructured data is one of the fastest-growing data types, but historically, there was no easy way to aggregate and analyze that data. To continue securely offering, discovering, and consuming all types of governed data, Snowflake acquired Applica, a purpose-built, multi-modal LLM for document intelligence.
Interactive data search
Snowflake’s recent acquisition of Neeva is accelerating data search through generative AI. It enables conversational paradigms for asking questions and retrieving information, allowing teams to discover precisely the right data point, data asset, or data insight.
Superior data security and governance
Snowflake is a leader in modern data security and governance. With robust security features built into the Data Cloud, including dynamic data masking and end-to-end encryption for data in transit and at rest, you can focus on analyzing your data, not protecting it. Snowflake complies with numerous government and data security compliance standards, having achieved Federal Risk & Authorization Management Program (FedRAMP) Authorization to Operate (ATO) at the Moderate level and StateRAMP Authorization at the High level. In addition, Snowflake supports ITAR compliance, SOC 2 Type 2, PCI DSS compliance, and HITRUST compliance.
Built for AI: Run Your Large Language Models in Snowflake
The Snowflake Data Cloud’s scalability, flexibility, and performance provide a powerful foundation for LLM-enabled machine learning applications. Snowflake paves the way for unlocking the capabilities of large language models, including enhanced language understanding, text generation, and advanced analytics at scale.
Learn more: Using Snowflake and Generative AI to Rapidly Build Features