Each architecture integrates with MongoDB Atlas core capabilities while leveraging external services (AWS Bedrock, OpenAI, Cohere) for AI/ML functionality, creating a comprehensive predictive maintenance solution.
Building the Solution
1. Set up MongoDB Atlas environment
a. Configure cluster, database and collections for machine failures, sensor data (raw and transformed), ML models, maintenance history, and repair documentation
b. Set up MongoDB Atlas Search and Vector search indexes for repair manuals and maintenance history
c.
d. Configure Stream Processing for real-time data transformation
e. Create Atlas Charts dashboards for monitoring and visualization
2. Configure AI services integration
a. Select one LLM provider for your implementation:
- Option 1 - AWS Bedrock: Configure access to Cohere models for embeddings and completions (Examples of available models: cohere.embed-english-v3 for embeddings, cohere.command-r-10 for completions)
- Option 2 - OpenAI: Set up API access and select appropriate model
b. Set up Google Cloud Translation API for multilingual support
3. Application setup
a. Configure environment variables including MongoDB connection strings, database settings, and required API credentials
b. Deploy inference script for continuous system monitoring
c. Install and configure alerts application
d. Launch main demo application
e. Perform system testing and validation to ensure proper data flow and functionality
For complete implementation details, including code samples, configuration files, and tutorial videos, visit the GitHub repository: https://github.com/mongodb-industry-solutions/Leafy-Predictive-Maintenance. This repository provides a production-ready template for implementing predictive maintenance using MongoDB Atlas' comprehensive feature set.
Key Learnings
MongoDB Atlas provides a unified platform for predictive maintenance by combining structured sensor data and unstructured maintenance documents, enabling both real-time monitoring and AI-powered analysis through a single-view architecture.
The solution leverages a four-stage approach (prioritization, prediction, plan generation, guidance delivery) that integrates multiple MongoDB features including Atlas Stream Processing for real-time data, Vector Search for semantic analysis, and Change Streams for mobile alerts.
Organizations can achieve significant operational improvements through this approach: 15-20% reduction in downtime, 5-20% increase in labor productivity, and 30-60% reduction in maintenance costs.
The implementation combines multiple AI technologies (RAG, LLMs, ML models) with MongoDB's developer data platform capabilities to create an automated maintenance workflow—from machine prioritization to mobile repair guidance delivery.
Technologies and Products Used
Partner technologies:
Authors
- Dr. Humza Akhtar, MongoDB
- Rami Pinto, MongoDB
- Sebastian Rojas Arbulu, MongoDB