Timecodes for "MLOps Zoomcamp 3.3 - Prefect Workflow"

[alexeygrigorev]

Youtube video: https://www.youtube.com/watch?v=x3bV8yMKjtc

[dimzachar]

Summary: In this video, we will learn how to productionize a Jupyter notebook into a Python script using Prefect. The video will start with a review of the code overview and data import for an ML model. We will then review the data read-in, feature engineering, and model training process. Next, we will add orchestration and observability with Prefect. The video will explain how to use caching and add decorators in ML flow. Finally, we will give an overview of using Prefect for data flow orchestration. Overall, this video will provide a comprehensive guide on how to use Prefect to productionize a Jupyter notebook into a Python script for an ML model.

Key Takeaways:

• The video is about productionizing a Jupyter notebook into a Python script using Prefect
• It covers a review of code overview and data import for an ML model, data read-in, feature engineering, and model training process
• It also explains how to add orchestration and observability with Prefect, use caching and decorators in ML flow, and gives an overview of using Prefect for data flow orchestration
• The video provides a comprehensive guide on how to use Prefect to productionize a Jupyter notebook into a Python script for an ML model.

Timestamps: 0:00:00 - Productionizing notebook into python script with Prefect. 0:03:06 - Review: Code overview and data import for ML model. 0:05:59 - Review: data read-in, feature engineering, model training. 0:08:43 - Adding orchestration and observability with Prefect. 0:11:42 - Explanation of using caching and adding decorators in ML flow. 0:14:50 - Overview of using Prefect for data flow orchestration.

[amitfrancis]

Updated the timecodes. Thank you!