MLOps with MLflow: Creating Time-series Models & Evaluating Models

Mlflow 2.3.2    |    Intermediate
  • 10 videos | 1h 23m 23s
  • Includes Assessment
  • Earns a Badge
MLflow integrates with Prophet, a powerful time-series model that considers seasonal effects. MLflow provides a variety of model evaluation capabilities, empowering you to thoroughly assess and analyze model performance. First, you will use Prophet in combination with MLflow for time-series forecasting. Integrating Prophet with MLflow's tracking capabilities, you will seamlessly manage and evaluate your time-series models. Running the Prophet model and viewing metrics will allow you to assess its forecasting performance. Cross-validation will enhance the evaluation process, ensuring reliability across different temporal windows. Then, you will use MLflow to evaluate machine learning (ML) models effectively. MLflow's evaluation capabilities, including Lift curves, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) curves, precision-recall curves, and beeswarm charts, provide valuable insights into model behavior and performance. Finally, you will use MLflow to configure thresholds for model metrics and only validate those models which meet this threshold.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Clean data for a time-series model
    Train a model and view the artifacts
    Perform cross-validation and evaluate model performance
    Clean data for machine learning (ml) and perform encoding
  • Create a machine learning model and set up model evaluation
    Run a model evaluation and analyze a lift curve
    Review a precision-recall curve and beeswarm charts
    Run a model and evaluate that model
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 33s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 10m 10s
    Find out how to clean data for a time-series model. FREE ACCESS
  • Locked
    3.  Training a Model and Viewing the Artifacts
    9m 33s
    Discover how to train a model and view the artifacts. FREE ACCESS
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    4.  Performing Cross-validation and Evaluating Performance
    9m 51s
    Learn how to perform cross-validation and evaluate model performance. FREE ACCESS
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    5.  Cleaning Data and Performing Encoding
    8m 36s
    Find out how to clean data for machine learning (ML) and perform encoding. FREE ACCESS
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    6.  Creating a Machine Learning Model and Setting Up Model Evaluation
    8m 58s
    Discover how to create a machine learning model and set up model evaluation. FREE ACCESS
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    7.  Evaluating a Model and Analyzing the Lift Curve
    9m 51s
    Learn how to run a model evaluation and analyze a lift curve. FREE ACCESS
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    8.  Understanding the Precision-Recall Curve and Beeswarm Charts
    11m 1s
    Upon completion of this video, you will be able to review a precision-recall curve and beeswarm charts. FREE ACCESS
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    9.  Using a Metric Threshold to Evaluate a Model
    11m 46s
    Learn how to run a model and evaluate that model. FREE ACCESS
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    10.  Course Summary
    2m 3s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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