--- title: AI Inference for Predictive Maintenance in Manufacturing Databricks description: predictive maintenance in manufacturing with AI inference and Databricks, reducing downtime, improving efficiency, enabling real-time equipment monitoring image: https://www.xenonstack.com/hubfs/ai-inference-for-predictive-maintenance-in-manufacturing-with-databricks.png --- - [![xenonstack-logo](https://www.xenonstack.com/hubfs/xenonstack-logo-new-relase.svg)](https://www.xenonstack.com/) - - Foundry Foundry Unified reasoning foundation enabling seamless orchestration, analytics, infrastructure, and trust across intelligent ecosystems [![pointers-icon](https://www.xenonstack.com/hubfs/xs-revamp-header-dropdown/our-purpose-line-icon.svg) Akira AI - Reasoning and Agent Orchestration Turn models into collaborative, policy-governed agents that learn and act together](https://www.xenonstack.com/agentic-platforms/akira-ai/) [![pointers-icon](https://www.xenonstack.com/hubfs/xs-revamp-header-dropdown/autonomous-operations-icon.svg) ElixirData - 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Get Started ![slider-cross-icon](https://www.xenonstack.com/hubfs/slider-cross-icon.svg) ## Interested in Solving your Challenges with XenonStack Team ## Get Started Get Started with your requirements and primary focus, that will help us to make your solution First Name \* Please enter a valid First Name Last Name \* Please enter a valid Last Name Business Email ID \* Please enter a valid Business Email ID Contact Number \* Please enter a valid Contact Number Company \* Please enter a valid Company Name Industry Belongs To \* Please Select your Industry Banking Fintech Payment Providers Wealth Management Discrete Manufacturing Semiconductor Machinery Manufacturing / Automation Appliances / Electrical / Electronics Elevator Manufacturing Defense & Space Manufacturing Computers & Electronics / Industrial Machinery Motor Vehicle Manufacturing Food and Beverages Distillery & Wines Beverages Shipping Logistics Mobility (EV / Public Transport) Energy & Utilities Hospitality Digital Gaming Platforms SportsTech with AI Public Safety - 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Our XenonStack Team will shortly reach out to you. We are looking forward to showcase how XenonStack can transform your business. ![usecase-banner (1)](https://www.xenonstack.com/hs-fs/hubfs/usecase-banner%20(1).webp?width=1921&height=622&name=usecase-banner%20(1).webp) [Elixirdata](https://www.xenonstack.com/blog/tag/elixirdata) # AI Inference for Predictive Maintenance in Manufacturing Databricks [Navdeep Singh Gill](https://www.xenonstack.com/blog/author/navdeep-singh-gill) | 23 January 2026 AI Inference for Predictive Maintenance in Manufacturing Databricks 13:05 ## What is AI Inference for Predictive Maintenance? In recent years, predictive maintenance has emerged as a key area of innovation for industries, especially in manufacturing. The advent of AI technologies has further revolutionised the traditional practices of maintenance management, enabling more efficient operations and less downtime.   This blog explores how AI inference, particularly with the help of [AI agents](https://www.xenonstack.com/blog/ai-agents), is reshaping predictive maintenance in manufacturing. We'll dive into how traditional methods are being superseded, the technology landscape, and how AI agents are unlocking new potential for predictive insights.  ## Key Concepts of Predictive Maintenance Predictive maintenance is the practice of using data-driven insights to predict when machinery or equipment will fail so that maintenance can be performed just in time, avoiding unplanned downtime and reducing maintenance costs. Traditionally, maintenance in manufacturing was reactive (fixing issues after they occurred) or preventive (regularly scheduled maintenance). With predictive maintenance, however, the goal is to anticipate failures before they happen.    **Key concepts within predictive maintenance include:**  - **Condition Monitoring**: Continuously tracking the condition of machinery using sensors and IoT devices. - **Data Analytics**: Analysing data from various sources to predict potential issues. - **Failure Prediction Models**: Using historical and real-time data to forecast when an equipment failure might happen. - **Downtime Minimisation**: Preventing costly production interruptions by performing maintenance only when necessary. By implementing predictive maintenance, manufacturers can optimize asset lifespan, reduce operating costs, and improve safety by addressing issues before they escalate.  ## Traditional Way of Maintenance in Manufacturing Before the widespread use of [Artificial Intelligence](https://www.xenonstack.com/blog/artificial-intelligence) and [Machine Learning](https://www.xenonstack.com/blog/machine-learning-tools), manufacturing relied heavily on reactive or scheduled preventive maintenance. Here's a look at the traditional approaches:  - **Reactive Maintenance**: This was the most basic approach, where maintenance was performed only when equipment broke down. This led to unexpected downtimes, production delays, and higher repair costs. - **Preventive Maintenance**: In this method, maintenance is carried out on a fixed schedule, whether the equipment needs it or not. While it reduced unexpected failures, it still led to inefficiencies, as it often involved replacing parts that were still in good condition. - **Time-Based Scheduling**: Manufacturing plants often operated with set schedules for inspections and maintenance, which was cost-effective in the short term but inefficient over time. If a piece of equipment was running smoothly, scheduled maintenance still took place, resulting in unnecessary downtime. These traditional methods failed to leverage the power of real-time data and predictive algorithms, which made it difficult to maximise uptime or optimise the use of resources effectively.  ## Impact on Customer Due to the Traditional Way of Maintenance The traditional maintenance approaches had several consequences that affected not only the operations of manufacturing plants but also the end customers:  - **Increased Downtime**: Reactive maintenance led to unpredictable downtime, negatively impacting production schedules and delivery timelines. - **Higher Operational Costs**: Regularly scheduled maintenance without data-driven insights often meant unnecessarily performing maintenance or replacing parts, leading to increased costs. - **Reduced Product Quality**: Downtime, machine breakdowns, and inefficient maintenance can affect the consistency of the final product, leading to customer dissatisfaction. - **Longer Lead Times**: Due to unpredictable failures, customers sometimes faced longer lead times, which affected the brand’s reliability. The lack of predictive insights in traditional systems made it difficult to address these issues promptly, leaving customers with delays and suboptimal product quality.  ## Prominent Technologies in the Space of Predictive Maintenance Predictive maintenance has evolved dramatically with the rise of several new technologies. Integrating AI, machine learning, and Iot devices has transformed how data is collected and analysed. Some of the key technologies in this space include:  - **Internet of Things (IoT)**: [IoT devices](https://www.xenonstack.com/insights/internet-of-things-workings) are crucial in predictive maintenance, as they collect real-time data from machinery and equipment through sensors. These devices measure variables such as temperature, pressure, vibration, and noise, which can indicate potential issues. - **Machine Learning (ML)**: Machine learning algorithms process historical data and identify patterns that can be used to predict equipment failures. These algorithms improve over time as they are fed more data. - **Cloud Computing**: Cloud platforms, like [Databricks](https://www.xenonstack.com/data-foundry/databricks/), provide the scalability and computational power needed to process large amounts of data in real time. They facilitate the creation, deployment, and management of predictive models. - **Edge Computing**: [Edge computing](https://www.xenonstack.com/blog/edge-computing) allows for data processing at the source (i.e., the equipment itself), reducing latency and enabling quicker decision-making when identifying potential failures. - **Big Data Analytics**: [Big data](https://www.xenonstack.com/blog/big-data-platform) technologies help in handling and processing large volumes of data from multiple sources, enabling more accurate predictive models and insights. These technologies have formed the foundation for modern predictive maintenance systems, providing manufacturers with the ability to proactively monitor and maintain their equipment.  ## How AI Agents Supersede Other Technologies in Predictive Maintenance Traditional technologies rely on human intervention or simplistic algorithms to perform maintenance forecasting. However, AI agents, or Agentic AI, bring next-level capabilities to predictive maintenance by using sophisticated techniques to continuously learn, adapt, and make decisions autonomously.  ### AI agents differ from traditional technologies in several keyways: - Continuous Learning & Real-Time Adaptation: AI agents leverage machine learning to continuously improve their predictive accuracy, making real-time decisions that optimize maintenance schedules without the need for manual updates. - Scalability & Efficiency: AI agents can process vast amounts of data from many machines simultaneously, allowing them to scale effectively in complex environments while automating tasks like scheduling repairs and ordering parts. - Context-Aware Decision Making: AI agents factor in a wide range of operational inputs, including environmental conditions and historical performance, to make more accurate and context-sensitive predictions for when maintenance is needed. - Cost Savings: AI agents help reduce maintenance costs by optimizing schedules, preventing unnecessary repairs, and extending the life of equipment, leading to lower operational expenses. - Improved Safety: By predicting equipment failures and potential hazards, AI agents help avoid accidents, ensuring a safer working environment for employees. - Sustainability Impact: AI-driven maintenance reduces waste and energy consumption, contributing to more sustainable operations by extending equipment lifespan and minimizing resource usage. ## Solution: AI Agents to Analyze Predictive Maintenance at Various Levels AI agents play a central role in enhancing predictive maintenance by analysing data at multiple levels and automating decision-making processes. Here’s a breakdown of the different types of AI agents involved in the predictive maintenance process:  - **Data Processing Agent**: This agent collects and preprocesses data from IoT sensors, equipment logs, and other sources. It is responsible for cleaning, transforming, and structuring the data so that other agents can process it effectively. - **Prediction Agent**: Using machine learning models, this agent analyses historical and real-time data to predict when equipment will fail or require maintenance. It uses time-series forecasting, regression models, and anomaly detection techniques. - **Decision-Making Agent**: After generating predictions, this agent determines the optimal course of action. For example, it might decide whether immediate maintenance is required, whether the system can run a few more cycles, or whether the machine should be taken offline for repairs. - **Optimization Agent**: This agent ensures that maintenance schedules are optimized to minimize downtime and maximize productivity. It uses optimization algorithms to find the best possible maintenance plan, considering resource availability and production constraints. - **Action Automation Agent**: Once a maintenance action is determined, this agent may automate the process, such as notifying the maintenance team, issuing a work order, or even placing an order for replacement parts based on inventory levels. These agents work together to create a seamless, end-to-end predictive maintenance system that helps manufacturers maintain operations at peak efficiency.  ![ai-agents-to-analyze-predictive-maintenance-at-various-levels](https://www.xenonstack.com/hs-fs/hubfs/ai-agents-to-analyze-predictive-maintenance-at-various-levels.png?width=1920&height=1080&name=ai-agents-to-analyze-predictive-maintenance-at-various-levels.png) Fig - AI Agents for Multi-Level Predictive Maintenance Analysis ## Successful Implementations of AI Agents in Predictive Maintenance Several companies have successfully implemented AI agents for predictive maintenance, leading to significant improvements in their operational efficiency. A notable example is **Siemens**, which has integrated AI-based predictive maintenance into their manufacturing plants using Databricks' cloud platform.  - **Siemens**: [Siemens](https://www.siemens.com/) uses AI-based predictive maintenance through Databricks’ cloud platform to process real-time sensor data from manufacturing equipment, predicting failures before they happen. This reduces unplanned downtime, optimizes parts replacement, and improves asset utilization. - **General Electric (GE)**: [**General Electric**](https://www.ge.com/) has implemented AI-driven predictive maintenance for industrial turbines and jet engines, reducing maintenance costs, extending equipment life, and preventing unscheduled downtime by predicting issues before they arise. - **Rolls-Royce**: [Rolls-Royce](https://www.rolls-roycemotorcars.com/en_GB/home.html) uses AI to predict the maintenance needs of its jet engines. By analyzing sensor data from engines in real-time, the company can forecast potential failures, enabling more efficient maintenance schedules and reducing operational disruption. - **Caterpillar**: [Caterpillar’s](https://www.caterpillar.com/) AI-powered system monitors heavy machinery and predicts failure points in equipment like excavators and bulldozers. This proactive approach allows for precise maintenance, minimizing costly repairs and maximizing uptime for construction companies. - **UPS**: [UPS](https://www.ups.com/track?loc=en_IN) uses AI to optimize the maintenance of its fleet of delivery vehicles. The system predicts vehicle breakdowns by analyzing sensor data, ensuring parts are replaced before failure occurs, which reduces downtime and maintenance costs. These real-world examples demonstrate the potential of AI agents to enhance the predictive maintenance processes in large-scale manufacturing environments significantly. ## The Future of AI Inference in Predictive Maintenance: Emerging Trends As AI continues to transform predictive maintenance, several key trends are shaping the future of manufacturing operations. Three major developments—self-healing machines, [AI-driven supply chain optimization](https://www.ibm.com/think/topics/ai-supply-chain), and the integration of edge AI with 5G—are expected to drive the next wave of innovation.  - Self-Healing Machines: The next step in predictive maintenance is the rise of self-healing machines. These systems will go beyond predicting failures and actively correct issues autonomously. By using AI and advanced sensors, machines will be able to identify problems and perform repairs without human intervention, reducing downtime and maintenance costs. - AI-Driven Supply Chain Optimization: Predictive maintenance will increasingly integrate with AI-driven supply chain optimization. AI will not only forecast equipment failures but also anticipate supply chain disruptions. This will allow manufacturers to adjust production schedules and inventory proactively, reducing lead times and preventing delays in production. - Edge AI with 5G: The combination of edge AI and 5G will enhance real-time predictive maintenance. With 5G's low latency, data can be processed directly on the factory floor, enabling faster decision-making and more immediate responses to equipment issues. Edge AI will further enable machines to analyze and act on data locally, improving efficiency and reducing the reliance on cloud infrastructure. In summary, the future of AI in [predictive maintenance](https://www.ibm.com/think/topics/predictive-maintenance) will bring more autonomous, interconnected systems that drive efficiency, reduce downtime, and optimize both machine health and the supply chain. These trends will ultimately transform manufacturing into a more resilient and proactive industry. ## Next Steps towards AI inference for Predictive Maintenance Talk to our experts about implementing AI inference for predictive maintenance in manufacturing with Databricks. Discover how industries leverage Agentic Workflows and Decision Intelligence to enhance real-time equipment monitoring, failure prediction, and operational efficiency. Learn how AI automates maintenance workflows, reduces downtime, and optimizes manufacturing operations for improved productivity and cost savings.  [Contact Us](https://www.xenonstack.com/contact-us/) [Talk To Specialist](https://www.xenonstack.com/talk-to-specialist/) ### More Ways to Explore Us ### [AI-Powered Data Quality Monitoring in Databricks](https://www.xenonstack.com/blog/quality-monitoring-databricks) ### [Leveraging Predictive Analytics for Improved Decision-Making](https://www.xenonstack.com/blog/predictive-analytics-decision-making) ### [Predictive Analytics for Food Safety and its Solution](https://www.xenonstack.com/use-cases/food-security)   ## Share Article - [![XenonStack Facebook](https://www.xenonstack.com/hubfs/xenonstack-facebook-service.svg)](http://www.facebook.com/share.php?u=https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks) - [![XenonStack Twitter](https://www.xenonstack.com/hubfs/xs-twitter-white-updated-icon.svg)](https://twitter.com/intent/tweet?text=I+found+this+interesting+blog+post&url=https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks) - [![XenonStack Linked In](https://www.xenonstack.com/hubfs/xenonstack-linkedin-service.svg)](http://www.linkedin.com/shareArticle?mini=true&url=https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks) - [![XenonStack Email Icon](https://www.xenonstack.com/hubfs/xenonstack-email-service.svg)](mailto:?subject=Check%20out%20https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks%20&body=Check%20out%20https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks) ## Table of Contents ## Share Article - [![XenonStack Facebook](https://www.xenonstack.com/hubfs/xenonstack-facebook-service.svg)](http://www.facebook.com/share.php?u=https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks) - [![XenonStack Twitter](https://www.xenonstack.com/hubfs/xs-twitter-white-updated-icon.svg)](https://twitter.com/intent/tweet?text=I+found+this+interesting+blog+post&url=https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks) - [![XenonStack Linked In](https://www.xenonstack.com/hubfs/xenonstack-linkedin-service.svg)](http://www.linkedin.com/shareArticle?mini=true&url=https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks) - [![XenonStack Email Icon](https://www.xenonstack.com/hubfs/xenonstack-email-service.svg)](mailto:?subject=Check%20out%20https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks%20&body=Check%20out%20https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks) ## Explore Related Topics [Decision Intelligence](https://www.xenonstack.com/blog/tag/decision-intelligence) [Cloud Native Applications](https://www.xenonstack.com/blog/tag/cloud-native-applications) [Generative AI](https://www.xenonstack.com/blog/tag/generative-ai) [Big Data Engineering](https://www.xenonstack.com/blog/tag/big-data-engineering) [FinOps](https://www.xenonstack.com/blog/tag/finops) [Data Foundry](https://www.xenonstack.com/blog/tag/data-foundry) [XAI](https://www.xenonstack.com/blog/tag/xai) [Autonomous Agents](https://www.xenonstack.com/blog/tag/autonomous-agents) [MetaSecure AI](https://www.xenonstack.com/blog/tag/metasecure-ai) ![navdeep-singh-gill](https://www.xenonstack.com/hubfs/navdeep-gill-ceo-xenonstack.svg) ## Navdeep Singh Gill Global CEO and Founder of XenonStack Navdeep Singh Gill is serving as Chief Executive Officer and Product Architect at XenonStack. 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Move from dashboards to autonomous, always-on analytics - ![Connects warehouses](https://www.xenonstack.com/hubfs/data-icon.svg)Connects to warehouses, lakes, and streaming sources - ![Question Answering](https://www.xenonstack.com/hubfs/answers.svg)Question-answering in natural language - ![Continuous monitoring](https://www.xenonstack.com/hubfs/monitoring-2.svg)Continuous monitoring for anomalies and KPI deviations [See in Action](https://elixirdata.co/) ![agentic-data-intelligence](https://www.xenonstack.com/hubfs/empowerment-of-analysts.svg) ✕ ## Intelligent Diagnostic for Self-Healing System Automation Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment - ![Diagnostics Icon](https://www.xenonstack.com/hubfs/diagnostic.svg)Automated diagnostics for recurring errors - ![Playbook Icon](https://www.xenonstack.com/hubfs/playbook.svg)Playbook execution: restart services, scale pods, clear queues - ![Feedback Icon](https://www.xenonstack.com/hubfs/feedback.svg)Feedback loop for improving remediation strategies [See in Action](https://agentanalyst.ai/) ![intelligent-diagnostic](https://www.xenonstack.com/hubfs/dataops-reimagined-banner-image.svg) ✕ ## Agentic GRC - Monitoring Risk and Compliance Controls AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready - ![Controls Icon](https://www.xenonstack.com/hubfs/controls.svg)Continuous control checks across infrastructure and SaaS - ![Audit Icon](https://www.xenonstack.com/hubfs/audit.svg)Automated evidence collection for audits - ![Risk Icon](https://www.xenonstack.com/hubfs/risk.svg)Risk scoring and prioritized remediation recommendations [Explore Agent GRC](https://agentgrc.ai/) ![monitoring-risk-and-compliance](https://www.xenonstack.com/hubfs/enhanced-security-measures.svg) ✕ ## Agentic Finance and Procurement Intelligent Agents Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter - ![Visibility Icon](https://www.xenonstack.com/hubfs/visibility.svg)Real-time visibility into spend and commitments - ![Anomaly Icon](https://www.xenonstack.com/hubfs/anomaly.svg)Anomaly detection on invoices and vendor performance - ![Workflow Icon](https://www.xenonstack.com/hubfs/workflow.svg)Intelligent workflows for approvals and sourcing decisions [Optimize Finance & Procurement](https://www.xenonify.ai/) ![agentic-finance-and-procurement](https://www.xenonstack.com/hubfs/responsible-ai-aviators-banner-illustration.svg) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Navdeep Singh Gill", "url" : "https://www.xenonstack.com/blog/author/navdeep-singh-gill" }, "dateModified" : "2026-01-23T05:44:41.892Z", "datePublished" : "2025-03-03T11:39:15.000Z", "headline" : "AI Inference for Predictive Maintenance in Manufacturing Databricks", "image" : [ "https://www.xenonstack.com/hubfs/ai-inference-for-predictive-maintenance-in-manufacturing-with-databricks.png" ], "mainEntityOfPage" : { "@id" : "https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://www.xenonstack.com/hubfs/Xenonstack-Serives%20-3-2-2021-10.png" }, "name" : "Xenonstack Inc" } } ``` ```json { "@context" : "http://schema.org", "@type" : "Organization", "address" : { "@type" : "PostalAddress", "addressCountry" : "USA", "addressLocality" : "Plano", "addressRegion" : "Texas", "postalCode" : "75024", "streetAddress" : "7700 Windrose Ave. " }, "description" : "XenonStack is the #1 Data and AI foundry and technology services company to simplify and scale Enterprise and Generative AI Journeys.", "email" : "business@xenonstack.com", "logo" : "https://f.hubspotusercontent30.net/hubfs/8161231/Xenonstack-Serives%20-3-2-2021-10.png", "mainEntityOfPage" : { "@id" : "https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks", "@type" : "WebPage", "description" : "predictive maintenance in manufacturing with AI inference and Databricks, reducing downtime, improving efficiency, enabling real-time equipment monitoring" }, "name" : "XenonStack", "sameAs" : [ "https://www.facebook.com/XenonStack", "https://www.linkedin.com/company/xenonstack/", "https://www.youtube.com/c/XenonStackOfficial", "https://twitter.com/xenonstack" ], "telephone" : "", "url" : "https://www.xenonstack.com/" } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Navdeep Singh Gill" }, "dateModified" : "January 23, 2026, 5:44:41 AM", "datePublished" : "2025-03-03 11:39:15", "description" : "predictive maintenance in manufacturing with AI inference and Databricks, reducing downtime, improving efficiency, enabling real-time equipment monitoring", "headline" : "AI Inference for Predictive Maintenance in Manufacturing Databricks", "image" : { "@type" : "ImageObject", "url" : "https://www.xenonstack.com/hubfs/ai-inference-for-predictive-maintenance-in-manufacturing-with-databricks.png" }, "mainEntityOfPage" : { "@id" : "https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://f.hubspotusercontent30.net/hubfs/8161231/Xenonstack-Serives%20-3-2-2021-10.png" }, "name" : "XenonStack" } } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.xenonstack.com/#org", "@type" : "Organization", "description" : "XenonStack provides AI-powered solutions for enterprises, focusing on automation, analytics, and infrastructure optimization.", "email" : "info@xenonstack.com", "logo" : { "@id" : "https://www.xenonstack.com/#logo", "@type" : "ImageObject", "url" : "https://www.xenonstack.com/assets/logo.png" }, "name" : "XenonStack", "sameAs" : [ "https://twitter.com/XenonStack", "https://www.linkedin.com/company/xenonstack", "https://www.facebook.com/xenonstack" ], "url" : "https://www.xenonstack.com/" } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.xenonstack.com/blog/ai-inference-for-predictive-maintenance-databricks#author", "@type" : "Person", "description" : "Global CEO and Founder of XenonStack; 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