RamiKrispin/pydata-ny-ga-workshop

Materials for the Deploy and Monitor ML Pipelines with Python, Docker and GitHub Actions workshop at the PyData NYC 2024 conference

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Emerging

This project helps data scientists and machine learning engineers create robust, automated systems for predicting future values based on incoming data. It takes raw data, such as real-time electricity demand from an API, processes it through machine learning models, and outputs forecasts, along with dashboards to monitor the process and model performance. This is ideal for those who need to operationalize their predictive models into continuous, self-updating pipelines.

Use this if you need to reliably deploy, monitor, and automatically refresh machine learning models that generate forecasts or predictions, ensuring they stay up-to-date with new data.

Not ideal if you're looking for a simple, one-off script for a prediction without needing continuous deployment, monitoring, or an automated pipeline.

predictive-analytics forecasting machine-learning-operations data-pipeline-automation energy-demand-prediction
No License No Package No Dependents
Maintenance 13 / 25
Adoption 9 / 25
Maturity 8 / 25
Community 8 / 25

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Last pushed

Mar 19, 2026

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