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MLflow

9 use cases using this technology

Financial ServicesFraud & Anomaly DetectionRecommendation & Personalization

Maya is simplifying banking for diverse populations

Maya

Maya, the Philippines' leading fintech company, partnered with Databricks to modernize its data architecture into a unified lakehouse on Delta Lake, centralizing 100% of its data for real-time access and AI scalability. Maya's ML-powered underwriting tools cumulatively disbursed $2.1 billion in loans by year-end 2024. AI-driven fraud detection, combining transactional sequence embedding, network analysis and predictive modeling, achieved up to a 98% reduction in fraud losses. Hyperpersonalized, AI-driven marketing campaigns increased average revenue per user (ARPU) by 45%. Using Unity Catalog for governance and automated ETL pipelines, Maya also achieved a 28% increase in storage capacity without a rise in costs.

EducationMachine LearningFraud & Anomaly DetectionAI Model Development & MLOps

GoGuardian: Safer schools, empowered teachers, thriving students

GoGuardian

GoGuardian, which powers safe, focused learning for half of U.S. K-12 students, migrated its ML infrastructure to Databricks to manage billions of daily inferences for web filtering, classroom management and harm prevention while maintaining a PII-free, COPPA/FERPA-compliant data environment. Using Delta Lake, Lakeflow, Unity Catalog, MLflow and Databricks Model Serving, GoGuardian achieved up to 50% reduction in machine learning operational costs, 90% operational cost savings with its Delphi website classification model, and a 62% reduction in inappropriate device use among students. AI-driven prioritization also cut the volume of records requiring human review for high-risk content by over 95%, from 1 million to 35,000-45,000.

RetailComputer VisionGenerative AILarge Language Models

Furniture.com Transforms Online Search with Databricks

Furniture.com

Furniture.com unifies over 60 retail partners and 1.5 million SKUs on the Databricks Data + AI Platform, using Delta Lake, MLflow and Unity Catalog to run its ML lifecycle. Its Find It AI product-discovery tool uses generative AI to create a synthetic image representing shopper intent, then matches it against the product catalog for image-based search. A Collections model uses LLMs to automatically group related products, finding more than 16,000 collections across 50+ partners with no human intervention. Users who interact with Find It AI show a click-through rate 8x higher than baseline and a return rate 3.2x higher than baseline.

TelecommunicationsMachine LearningConversational AI

Building the foundation for the 5G revolution: Digital Nasional Berhad

Digital Nasional Berhad

Digital Nasional Berhad (DNB), Malaysia's government-established 5G network operator, used the Databricks Data + AI Platform to build a cost-effective, high-performance data platform to process 5G network data at scale, achieving 82%+ populated-area coverage in two years. Using Delta Lake with a medallion (Bronze/Silver/Gold) architecture, Databricks Workflows for pipeline orchestration, serverless compute, Notebooks for team collaboration, Unity Catalog for governance, and MLflow for ML lifecycle management, DNB achieved 70% cost optimization and a 60-70% increase in data pipeline performance, and has begun building AI-powered chatbots giving network engineers real-time access to performance insights with geospatial visualizations.

Technology & SoftwareRetrieval-Augmented GenerationAI Model Development & MLOps

Zapier Powers Real-Time Customer Experiences with Databricks

Zapier

Zapier unified its data on the Databricks Platform to enable self-serve, AI-powered analytics across product, marketing, sales and support. Using the Databricks MCP connector built on Unity Catalog, AI agents get governed real-time access to Zapier's data; Vector Search handles over 600k requests per day for semantic search and RAG-based knowledge retrieval; MLflow and Model Serving operationalize ML workflows. Dashboard creation time dropped from 2-3 days to a few hours, and the share of data queries executed via AI-assisted workflows grew from 0% to roughly 75% within 6-9 months.

ManufacturingPredictive AnalyticsMachine LearningAgentic AI

TK Elevator: Moving Beyond with Data + AI Driven Service Intelligence

TK Elevator

TK Elevator (TKE), one of the world's largest providers of elevators, escalators and vertical transportation systems, adopted the Databricks Platform to unify and process more than 500 million IoT telemetry events daily from connected elevators across 30+ countries, moving from reactive to predictive maintenance. TKE's MAX platform generates predictive insights that feed actionable guidance to field technicians via its Digital Operations Center (DOC), which generates 30,000-40,000 tickets annually and has reduced technician troubleshooting time on call-backs and unplanned shutdowns by up to 20%. The platform scaled from 3 to over 40 markets (10x expansion), cut data pipeline delivery from months to weeks, and reduced data sharing time with internal/external consumers from three months to three days (90% reduction). TKE uses Delta Lake, Delta Sharing, Lakeflow, Unity Catalog, Databricks Assistant, MLflow, AI Gateway, Vector Search and Model Serving, and is exploring agentic AI to combine technical, service and commercial context in real time.

Financial ServicesMachine LearningGenerative AINatural Language Processing

Techcombank: Ushering Personalized Banking for Millions of Customers

Techcombank

Techcombank, Vietnam's largest private financial institution with 315 branches serving over 15.3 million customers, adopted the Databricks Data + AI Platform to unify data from disparate on-premises databases and a legacy data warehouse. Central to the initiative is 'Customer Brain,' a customer-360 tool centralizing customer data for targeted marketing, and the Lead Allocation Curated Engine (LACE), which uses AI insights to prioritize and assign sales leads. Techcombank built machine learning models for fraud detection and credit risk management, using an enterprise feature store with over 7,500 features and more than 100 ML risk models to manage a credit lifecycle handling a twentyfold increase in retail credit applications. MLOps on Databricks with MLflow cut model implementation from months to weeks. The bank is developing an internal RAG-based chatbot, Smartie, built on Databricks AI Search, and piloting AI/BI Genie for natural-language data queries. The platform has over 1,000 active users bank-wide.

RetailGenerative AIAgentic AI

How 7-Eleven, Inc. Built a Game-Changing GenAI Creative Assistant for Marketing with Databricks

7-Eleven, Inc.

7-Eleven's AI Center of Excellence built a multi-agent GenAI marketing assistant using LangGraph and Databricks, moving beyond early document-based chatbots and basic RAG. The system includes a Campaign Creative Generator, Copywriter Bot, General Assistant, and Supervisor Agent that orchestrates workflow and reviews outputs, with human-in-the-loop review, web search for trend intelligence, and guardrails filtering toxicity, PII and off-policy responses. Databricks provides governance and observability via MLflow tracking, automated agent evaluation, and inference tables. The assistant reduced manual creative hours, with campaign concepting and scripting that used to take hours or days now generated, refined, and approved in minutes; user feedback included calling it 'a game changer' and 'better than ChatGPT.'

ManufacturingRetrieval-Augmented GenerationConversational AINatural Language Processing

Lippert Improves Customer Support with GenAI

Lippert

Lippert, a $3.8 billion global manufacturer of components for RV, marine and automotive brands, used Databricks and Agent Bricks to consolidate fragmented data into a single lakehouse and deploy AI agents for customer support. An AI assistant trained on product manuals, technical case history and field-expert videos surfaces troubleshooting guidance for call center agents in real time, cutting new-agent ramp time from six months to four weeks (an 85% reduction). Using Agent Bricks' evaluation framework and synthetic data, model accuracy for the support agent improved from 33% to 84% within weeks. AI also analyzes thousands of support calls daily for coaching and quality scoring (versus a prior manual sample of 100 calls/month) and automates call summarization into Salesforce. The initiative is expected to save millions per year and reclaim hundreds of thousands of hours, with new agents planned for HR, warranty and supply chain.