Unlocking Customer Insights at Scale: Batch Inference with LLMs on Databricks Modern organizations collect vast amounts of customer feedback through surveys, support tickets, and product reviews. However, this feedback is often unstructured, multilingual, and difficult to process manually. Batch inference, a method of applying machine learning models to large datasets all at once, offers a scalable solution. By leveraging Databricks' AI platform and generative AI models like LLaMA 3, companies can automate the translation, classification, and summarization of customer feedback, leading to faster insights and data-driven decisions. What Is Batch Inference with LLMs on Databricks? Batch inference involves processing large volumes of data in bulk, as opposed to real-time streaming. This is particularly useful for handling backlogs of unstructured data or for regular, periodic data analysis tasks like weekly survey results or monthly customer reviews. Databricks simplifies batch inference by providing SQL-native functions like ai_translate() and ai_query(), enabling teams to apply these models directly within their workflows without needing to set up external APIs or manage custom ML infrastructure. Setting the Stage: The Sample Survey To illustrate this workflow, a sample survey was created using Google Forms, featuring five questions: 1. How satisfied are you with our service? 2. What did you like most? 3. What could we improve? 4. How likely are you to recommend us to a friend or colleague? 5. Any additional comments? The survey responses were intentionally diversified to include English, Spanish, Chinese, French, and Japanese, simulating real-world feedback scenarios. The responses were stored in a series of CSV files, which were then uploaded to a Databricks Volume for incremental ingestion. Step 1: Incremental Ingestion with Auto Loader Auto Loader, a feature in Databricks, was used to ingest the survey data into a Delta table incrementally. This ensures each new file is processed exactly once, supports schema evolution, and is ideal for production pipelines. The raw dataset, consisting of mixed languages and inconsistent formats, was loaded, setting the stage for further processing. Step 2: Translate Responses with ai_translate() The next step involved translating non-English feedback into English using Databricks' ai_translate() function. This function converts text across multiple languages, providing both the original and translated versions. This transparency helps maintain traceability and ensures the integrity of the data. Step 3: Normalize and Prepare Data for AI Once the responses were translated, a view was created to normalize the data, ensuring all records used English as the standard language. This step is crucial for consistent and reliable AI analysis. By coalescing the original and translated columns, the dataset was prepared for batch inference. Step 4: Generate Insights with ai_query() and LLaMA 3 Using Databricks' ai_query(), natural language prompts were embedded directly into SQL queries to generate insights from the customer responses. The LLaMA 3 model was employed to extract specific details: - Sentiment: Positive, negative, or neutral feedback. - Topics: Key issues or themes mentioned by customers. - Actionable Items: Specific suggestions for improvement. This approach allows product managers, analysts, and support teams to quickly understand customer sentiments and prioritize actions based on real data, enhancing decision-making processes. Step 5: Visualize Results in a Dashboard The final step involved creating a dashboard using Databricks' SQL editor to make the insights easily accessible to non-technical stakeholders. Business users can explore trends over time, highlight areas for improvement, and identify customer pain points without needing to write code. The dashboard updates automatically as new survey files are ingested, ensuring up-to-date and relevant insights. Why This Approach Works This workflow revolutionizes the way companies process and analyze unstructured, multilingual feedback. By automating translation and insightful extraction, it significantly reduces the time and effort required to turn raw data into actionable insights. Key benefits include: - Efficiency: Processing large datasets in bulk, minimizing manual intervention. - Accuracy: Reliable and consistent insights generated by powerful AI models. - Transparency: Maintaining the original and translated versions for traceability. - Accessibility: Making insights available to non-technical teams via an intuitive dashboard. Industry Evaluation and Company Profile Industry experts praise Databricks' integration of AI with SQL for its ability to bridge the gap between data scientists and business users. By encapsulating complex AI operations within simple SQL commands, Databricks democratizes access to advanced analytics, making it easier for organizations to leverage AI in their daily operations. Databricks, a leader in the big data and machine learning space, continues to innovate by providing scalable, user-friendly solutions that enhance data-driven decision-making. This workflow, which can be adapted for various applications such as product reviews, support tickets, and internal feedback, demonstrates Databricks' commitment to offering practical, impactful tools. Companies looking to extract maximum value from their qualitative data can benefit greatly from implementing this batch inference process.