Many application domains increasingly require AD, when anomalies carry critical and actionable information. These include: (1) Cyber-security and intrusion detection in Cloud and IT systems, also in government, defense and security agencies; (2) Fraud detection in financial institutions; (3) Manufacturing, IoT, industry and resource exploration; (4) Healthcare; etc. We shall address the problem of detecting and predicting general anomalies in high-dimension KPI performance metrics, i.e., high dimension and dynamic range multivariate non-stationary time series collected from large Cloud / IT environments. Using Keras / TF etc., we will build an ML-based AD framework for transfer, attention and meta-learning that must remain robust also with reduced/missing and noisy training data. Besides feature engineering – e.g., selection, reduction, compression techniques – explainability will also be necessary for the model prototype.