Deep Learning for Genomics

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Model Deployment and Monitoring

The primary goal of Machine Learning (ML) and Deep Learning (DL) is to build predictive models and get insights from the data to help solve business problems. However, a trained model can only do that once you take your model and turn it into a production environment – a process referred to as model deployment. A deployed model enables other researchers (either within your organization or outside) to interact with and extract the most value out of it. Many models don’t end up in the production environment purely because of technical challenges. Once a model is put into the production environment, constant attention is required on an ongoing basis to detect any drifts or anomalies for the success of a DL project ...