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
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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
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1m 33sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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10m 10sFind out how to clean data for a time-series model. FREE ACCESS
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3. Training a Model and Viewing the Artifacts9m 33sDiscover how to train a model and view the artifacts. FREE ACCESS
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4. Performing Cross-validation and Evaluating Performance9m 51sLearn how to perform cross-validation and evaluate model performance. FREE ACCESS
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5. Cleaning Data and Performing Encoding8m 36sFind 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 Evaluation8m 58sDiscover 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 Curve9m 51sLearn 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 Charts11m 1sUpon 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 Model11m 46sLearn how to run a model and evaluate that model. FREE ACCESS
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10. Course Summary2m 3sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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