From Regression to Reasoning — A brief & Use cases by industry verticals

 

Traditional ML and the Rise of Large Language Models

Today, as I leave SuperComputing 2024, I find ourselves at a fascinating juncture. Traditional machine learning (ML), the backbone of early AI breakthroughs, stands alongside Large Language Models (LLMs), which are undeniably the glittering stars of today’s AI landscape. But while LLMs dazzle us with their ability to generate poetry, write code, and mimic human conversation, traditional ML has quietly remained a workhorse, excelling in areas where flashiness isn’t the goal but precision and efficiency are. The coexistence of these paradigms raises an intriguing question: where does traditional ML shine in the LLM era, and what does the future hold for these trusty algorithms?

Traditional ML encompasses a gamut of algorithms, from linear regression to decision trees, support vector machines (SVMs), and clustering methods like k-means. These models are optimized for structured data, often working wonders in scenarios where relationships between inputs and outputs can be captured succinctly. Need to predict house prices based on square footage and location? Regression has your back. Want to segment customer data for targeted marketing? Clustering delivers. These algorithms excel in environments where interpretability, computational efficiency, and domain-specific tuning are paramount. Unlike LLMs, which gulp down terabytes of unstructured data, traditional ML can thrive on a few well-curated features.

In contrast, LLMs, such as GPT or LLaMA, are built to process and generate unstructured data at scale. They are the Swiss Army knives of AI, performing tasks ranging from sentiment analysis to summarization without requiring much customization for specific use cases. This versatility comes at a cost: LLMs are computationally hungry and notoriously opaque in their decision-making. While you can interrogate a linear regression model to understand how each variable influences the outcome, LLMs often feel like inscrutable black boxes.

Where Traditional ML Shines in an LLM World

Despite the hype surrounding LLMs, traditional ML is far from obsolete. Its strengths lie in structured data processing, real-time decision-making, and low-latency environments. Consider fraud detection in banking: a decision tree or an ensemble method like XGBoost can quickly flag anomalies in transactional data, offering interpretability that auditors demand. Similarly, recommendation engines, logistic regressions, and SVMs are still staples in industries like e-commerce and healthcare, where every millisecond matters.

Traditional ML plays a crucial role in preprocessing and orchestrating workflows for LLMs. Before an LLM can generate a coherent answer or summarize a legal brief, traditional ML algorithms often filter, categorize, or prioritize data. These “routers” and “decision-makers” act as gatekeepers, ensuring the right data gets to the LLM and the outputs are routed to the appropriate downstream tasks.

Incorporating Traditional ML with LLMs

The real magic happens when these paradigms collaborate, especially in agentic workflows. Imagine a customer support system where an LLM generates personalized responses based on user queries. Behind the scenes, traditional ML models classify incoming tickets into categories, rank their urgency, and assign them to the appropriate agents or bots. In this workflow, traditional ML serves as the analytical mind, handling the structured, deterministic tasks, while the LLM brings conversational flair.

Agentic workflows go a step further by introducing reasoning and decision-making loops. Here, we see a blend of hierarchical models, where traditional ML acts as the router, passing contextual information to an LLM that acts as a decision-maker. For instance, in autonomous vehicles, traditional ML models might process sensor data to detect objects, while an LLM-like agent interprets broader contexts — navigating ambiguous instructions like “Find the closest parking spot” in real time.

The Future of Traditional ML

As LLMs evolve, traditional ML isn’t simply standing still, pining for its glory days. Instead, it’s advancing in areas like online learning, federated learning, and edge computing. Algorithms are becoming more adaptive, learning incrementally as new data arrives, and more efficient, thriving in environments where bandwidth and computational resources are limited. In edge AI, where LLMs might be too bulky to deploy, lightweight traditional ML algorithms are indispensable.

Moreover, the principles behind traditional ML — modularity, explainability, and resource efficiency — are influencing how we build and refine LLMs. Techniques like fine-tuning and low-rank adaptation owe much to the foundational work done in feature selection and optimization in traditional ML. In many ways, the progress of traditional ML and LLMs is symbiotic.

The Road Ahead

The future isn’t a zero-sum game where one paradigm outlasts the other. Instead, it’s a collaborative ecosystem where traditional ML and LLMs complement each other’s strengths. As industries like healthcare, finance, and manufacturing adopt agentic workflows, we’ll see increasingly sophisticated interactions between these paradigms. Traditional ML will act as the spine, providing structure, while LLMs, like the nervous system, bring flexibility and responsiveness.

The moral of the story? Don’t count traditional ML out. While LLMs may be the show-stopping protagonists of this era, the steady and reliable algorithms of traditional ML remain indispensable sidekicks, quietly getting the job done with elegance and efficiency. And who knows — perhaps in their collaboration, we’ll find the seeds of the next great leap forward in AI.

Use Cases for Traditional ML and LLMs in Agentic Workflows Across Industry Verticals

The marriage of traditional ML models and Large Language Models (LLMs) within agentic workflows is already proving transformative across a variety of industries. Below are examples from major verticals where these paradigms complement each other to achieve remarkable outcomes.

Healthcare

Use Case: Patient Diagnosis and Treatment Recommendations

• Traditional ML’s Role: Predictive models, such as decision trees or random forests, analyze structured patient data like lab results, vital signs, and medical history. These models flag anomalies or predict the likelihood of conditions like diabetes or cardiovascular disease.
• LLM’s Role: LLMs take this output and contextualize it, generating detailed treatment recommendations, translating medical jargon into patient-friendly language, or summarizing research papers for physicians.
• Workflow Example: A hospital system uses an ML-powered model to detect abnormalities in an ECG. The results are routed to an LLM-powered assistant, which drafts an initial diagnostic report and suggests possible interventions based on recent medical literature.

Finance

Use Case: Fraud Detection and Customer Support

• Traditional ML’s Role: Logistic regression or ensemble methods analyze structured transaction data for suspicious patterns, flagging potential fraud in near real-time.
• LLM’s Role: An LLM, integrated into a chatbot, communicates these findings to the customer in an empathetic and user-friendly manner, explaining why a transaction was blocked and guiding the user through the resolution process.
• Workflow Example: A financial institution employs an ML model to identify unusual spending patterns on a credit card. The LLM sends an alert to the customer, offering an interactive Q&A session to verify legitimate transactions or escalate concerns.

Retail and E-Commerce

Use Case: Personalized Shopping Experiences

• Traditional ML’s Role: Recommendation systems powered by collaborative filtering or clustering group customers based on purchase histories and browsing behavior to suggest products.
• LLM’s Role: LLMs analyze unstructured customer reviews and queries to provide detailed product descriptions, answer questions, or generate personalized marketing emails.
• Workflow Example: A recommendation engine suggests a category of products to a shopper. An LLM uses this information to generate a customized message like, “Based on your interest in running gear, here are some tips for choosing the perfect pair of shoes.”

Manufacturing

Use Case: Predictive Maintenance and Knowledge Management

• Traditional ML’s Role: Time-series models analyze sensor data from machinery to predict potential failures or maintenance needs.
• LLM’s Role: LLMs enhance operational efficiency by creating comprehensive reports, generating instructions for maintenance tasks, or answering technician queries in natural language.
• Workflow Example: An ML algorithm predicts that a conveyor belt motor is nearing failure. An LLM drafts a maintenance request, including step-by-step repair instructions, and answers technician questions in real time.

Automotive

Use Case: Autonomous Vehicles and Navigation

• Traditional ML’s Role: Object detection algorithms like YOLO or decision trees process structured data from sensors, identifying objects, and classifying them as pedestrians, vehicles, or obstacles.
• LLM’s Role: LLMs assist with unstructured tasks, such as interpreting vague verbal commands from passengers (“Take me to a scenic spot”) and integrating broader context into route planning.
• Workflow Example: An ML-powered vision model identifies a cyclist in the vehicle’s path. The LLM interprets a passenger’s request to avoid heavy traffic, using the real-time object detection data to plan an alternate route.

Energy

Use Case: Smart Grid Management

• Traditional ML’s Role: Predictive models analyze energy consumption patterns to optimize load distribution and forecast demand.
• LLM’s Role: LLMs facilitate user interaction by summarizing energy usage patterns for consumers or creating alerts for system administrators about grid issues.
• Workflow Example: An ML model predicts an upcoming energy surge based on historical weather patterns. The LLM generates and distributes tailored conservation tips to households in affected areas, explaining how reducing usage at specific times could prevent outages.

Life Sciences

Use Case: Drug Discovery and Literature Review

• Traditional ML’s Role: Clustering and classification models identify patterns in biochemical datasets, narrowing down potential drug candidates.
• LLM’s Role: LLMs parse vast repositories of scientific literature, summarizing findings, generating hypotheses, or proposing experimental protocols.
• Workflow Example: An ML model identifies promising compounds based on molecular properties. An LLM generates a research report summarizing relevant studies, complete with citations, for the R&D team.

Logistics and Supply Chain

Use Case: Demand Forecasting and Dynamic Scheduling

• Traditional ML’s Role: Time-series forecasting models predict demand based on historical shipment data, helping allocate resources efficiently.
• LLM’s Role: LLMs handle unstructured inputs like customer emails or feedback to identify potential delays or generate custom solutions for urgent requests.
• Workflow Example: An ML model predicts a surge in demand for a specific product. The LLM generates a supply chain strategy memo, considering potential vendor delays and providing human-readable explanations for distribution adjustments.

Entertainment and Media

Use Case: Content Recommendation and Curation

• Traditional ML’s Role: Collaborative filtering models predict viewer preferences based on structured user behavior data, such as viewing history and ratings.
• LLM’s Role: LLMs generate personalized content descriptions, summaries, or even automated scripts for upcoming shows based on user interests.
• Workflow Example: Netflix uses an ML recommendation engine to suggest shows. An LLM adds value by generating conversational episode summaries or answering subscriber questions like, “What makes this show similar to my favorite series?”

Telecommunications

Use Case: Network Optimization and Customer Service

• Traditional ML’s Role: Predictive algorithms analyze network traffic to detect bottlenecks, optimize bandwidth allocation, and predict outages.
• LLM’s Role: LLMs act as conversational agents, assisting customers in resolving network issues or explaining technical details in layperson’s terms.
• Workflow Example: An ML model identifies a potential overload in a specific cell tower area. The LLM reaches out to customers with tailored messages suggesting alternative usage times or steps to troubleshoot potential connectivity issues at home.

Education

Use Case: Adaptive Learning Platforms

• Traditional ML’s Role: Classification algorithms assess student performance, identifying areas of strength and weakness based on structured test scores and interaction data.
• LLM’s Role: LLMs generate customized learning content, answer student queries in real time, and even provide detailed explanations of complex concepts.
• Workflow Example: An ML model flags that a student is struggling with algebra. The LLM crafts personalized practice problems, provides feedback, and explains errors in the student’s work interactively.

Government and Public Sector

Use Case: Policy Analysis and Public Communication

• Traditional ML’s Role: Clustering models group public feedback into actionable categories for policy analysis, while predictive models forecast the impact of proposed policies.
• LLM’s Role: LLMs synthesize reports for policymakers, generate summaries of proposed bills, and engage with the public by answering questions in natural language.
• Workflow Example: A local government uses ML to analyze public comments on urban planning initiatives. An LLM creates an executive summary for decision-makers and provides an FAQ for citizens, explaining how proposed changes will affect their neighborhoods.

Agriculture

Use Case: Precision Farming and Crop Management

• Traditional ML’s Role: Regression models analyze structured sensor data, such as soil moisture, temperature, and pH levels, to optimize irrigation schedules and fertilizer use. These models predict crop yields, identify potential disease outbreaks, and suggest corrective actions based on quantifiable metrics.
• LLM’s Role: LLMs process unstructured data, such as farmers’ notes, weather reports, market trends, and research articles, to provide insights on optimal crop selection, pest control strategies, or sustainable farming practices. They also serve as conversational agents, answering farmers’ queries and generating tailored advice based on context.
• Workflow Example: A precision agriculture platform employs ML models to monitor soil and weather data, identifying that a field requires additional irrigation. The LLM then interprets recent agricultural research to recommend specific water-saving techniques and drafts an easy-to-follow action plan for the farmer, incorporating both the structured data insights and broader context like upcoming weather conditions or local market demands.

As I work across thousands of customers, I try to cultivate common and unique use cases across the industry verticals.