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Colocation Data Centers: An Overview of Predictive Maintenance (Part 3 of 3)

Venkatesh Subramanian
5 min readDec 10, 2024

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There are numerous traditional and deep-learning models used to predict potential failures and detect anomalies. This post compares a few of them. You can also explore pre-trained models and research papers for a deeper understanding.

sample ml training pipeline

The above is a sample training pipeline. You can use any of the MLOps frameworks, such as MLflow, Kubeflow, or any cloud provider’s AI platform. Common MLOps practices ensure reproducibility and repeatability and involve storing and versioning artefacts (machine learning models, training configurations, and evaluation metrics).

Please also check Part 1 and Part 2 which discusses the data collection and data pipeline.

Model Selection

Traditional models like Random Forests and Isolation Forests help detect anomalies in the data.

Random cut forest /Isolation Forest

Unlabeled data: RCF can work with normal data only, but including anomaly data can improve performance. If RCF is trained on a mix of normal and anomaly data, it would require labelled data to differentiate between the two. Isolation Forest is an unsupervised model.

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Venkatesh Subramanian
Venkatesh Subramanian

Written by Venkatesh Subramanian

Product development & Engineering Leader| Software Architect | AI/ML | Cloud computing|https://www.linkedin.com/in/venkatesh-subramanian-377451b4/

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