Use Machine Learning and GridDB to build a Production-Ready Stock Market Anomaly Detector

In this project, we use GridDB to create a Machine Learning platform where we Kafka is used to import stock market data from Alphavantage, a market data provider. Tensorflow and Keras train a model that is then stored in GridDB, and then finally uses LSTM prediction to find anomalies in daily intraday trading history. The last piece is that the data is visualized in Grafana and then we configure GridDB to send notifications via its REST Trigger function to Twilio's Sendgrid. The actual machine learning portion of this project was inspired by posts on Towards Data Science and Curiously. This model and the data flow is also applicable to many other datasets such as predictive maintenance or machine failure prediction or wherever you want to find anomalies in time series data.