AI based monitoring and decision making solutions

Based on our approach we implemented PCA, Isolation Forest and Autoencoder based anomaly detection models to identify anomalies from the good service. Hence there is a chance of high false-positive cases if we depend on a single model, we implemented two models in production and took cumulative inference for decision making. The time-series call data forecasting was achieved by training an LSTM model on historical volume data and to forecast for the desired time in the future. Notably, these two solutions are real-time which required a high level of optimization to accommodate the high frequency of incoming data. We deployed the models using Kubernetes and OKD deployment frameworks coupled with NVidia GPUs for high-performance model training.