Machine learning requires experimenting with datasets, data preparation steps, and algorithms. Deploy models to a production system and retrain it on new data. MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
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Instructions and data can be found here: https://github.com/tsterbak/pydataberlin-2019
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
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Domains:Data Science, Infrastructure, Machine Learning, Data Engineering
Python Skill Level:basic
Abstract as a tweet:How to manage the end-to-end machine learning lifecycle with MLflow.
Link to talk slides: Public link to supporting material: