Tobias Sterbak

Data protection has grown more and more important as the amount of critical data that is being stored, analyzed or send via the Internet increases exponentially. So, how can developers help protect the data they work with? How can algorithms analyze data while also ensuring the privacy of the user? This meetup has two top developers explain exactly which methods they use to anonymize or otherwise protect data that is used in advanced machine learning algorithms.

Machine learning requires experimenting with a wide range of datasets, data preparation steps, and algorithms to build a model that maximizes some target metric. Once you have built a model, you also need to deploy it to a production system, monitor its performance, and continuously retrain it on new data and compare with alternative models. A possible solution to managing this complexity is offered by MLFlow. MLflow is an open source platform for managing the end-to-end machine learning lifecycle. This tutorial showcases how you can use MLflow end-to-end to: - Train models and keep track of experiments with MLflow Tracking - Package the code that trains the model in a reusable and reproducible model format with MLFlow Projects - Deploy the model into a HTTP server that will enable you to score predictions with MLFlow Models