314 points by anomaly-eye 2 years ago flag hide 13 comments
johnsmith 2 years ago next
Fascinating article on ML-based anomaly detection. I've been using similar methods to monitor my large-scale systems and the results have been impressive.
machinelearner 2 years ago next
Glad to hear that it's working well for you, johnsmith. I'm curious about the specific ML models you're using for your system. Could you provide some more details?
johnsmith 2 years ago next
Sure thing, machinelearner! I'm using a combination of Random Forests and LSTM networks to detect anomalies. I find that this approach captures both short and long-term patterns in my system's behavior.
machinelearner 2 years ago next
Interesting combination of models, johnsmith. Have you considered using any unsupervised methods like autoencoders or clustering techniques to complement your supervised approach?
johnsmith 2 years ago next
That's a good point, machinelearner. I have been thinking about incorporating some unsupervised methods into my approach. I'll give autoencoders a try and see how they perform.
johnsmith 2 years ago next
I'm glad to hear that, statsgeek. I'll keep that in mind when evaluating the performance of my autoencoder implementation. Thanks for the advice!
anonymous 2 years ago prev next
I'm skeptical about relying solely on ML-based approaches for anomaly detection. They can be prone to false positives and negatives.
statsgeek 2 years ago next
Anomaly detection is a complex problem and no single approach is perfect. However, ML-based methods can be quite effective if properly implemented and validated with appropriate metrics.
bigdatafan 2 years ago prev next
I recently implemented an autoencoder-based anomaly detection system for a large-scale data ingestion pipeline. It's been working great so far.
statsgeek 2 years ago next
Autoencoders can be very effective for detecting anomalies in high-dimensional data, bigdatafan. How did you validate your model's performance?
bigdatafan 2 years ago next
I used a combination of holdout validation and time-series cross-validation to evaluate my autoencoder's performance, statsgeek.
optimizationexpert 2 years ago prev next
It's also important to consider the trade-off between false positives and false negatives in anomaly detection. Different use cases may require different approaches.
machinelearner 2 years ago next
That's a great point, optimizationexpert. The cost of false positives and false negatives can vary greatly depending on the specific use case. It's important to take that into account when tuning anomaly detection models.