Abhi0323/Agile-MLOps-Deployment-Docker-AWS-CI-CD-Pipeline
An end-to-end predictive maintenance application using machine learning to enhance industrial efficiency. This project employs robust modular architecture and advanced MLOps practices, including Docker and AWS for scalable, real-time maintenance predictions.
This project helps operations engineers and plant managers predict when industrial equipment will need maintenance, avoiding costly breakdowns. It takes historical sensor data and equipment performance logs as input to provide real-time predictions about potential failures. The output is a web-based dashboard showing maintenance predictions, enabling proactive scheduling and improved operational efficiency.
No commits in the last 6 months.
Use this if you manage industrial equipment and need to shift from reactive to proactive maintenance based on data-driven predictions.
Not ideal if your organization lacks consistent sensor data from machinery or if your primary concern is not equipment uptime.
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35
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9
Language
Python
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Last pushed
Apr 18, 2024
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