MLOps with MLflow: Creating & Tracking ML Models
Mlflow 2.3.2
| Intermediate
- 15 videos | 1h 45m 24s
- Includes Assessment
- Earns a Badge
With MLflow's tracking capabilities, you can easily log and monitor experiments, keeping track of various model runs, hyperparameters, and performance metrics. In this course, you will dive hands-on into implementing the ML workflow, including data preprocessing and visualization. You will focus on loading, cleaning, and analyzing data for machine learning. You will visualize data with box plots, heatmaps, and other plots and use the Pandas profiling tool to get a comprehensive view of your data. Next, you will dive deeper into MLflow Tracking and explore features that enhance experimentation and model development. You will create MLflow experiments to group runs and manage them effectively. You will compare multiple models and visualize performance using the MLflow user interface (UI), which can aid in model selection for further optimization and deployment. Finally, you will explore the capabilities of MLflow autologging to automatically record experiment metrics and artifacts and streamline the tracking process.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Load, clean, and visualize data for machine learning
View statistics about data with pandas profiling and use it to view correlations
Create an mlflow experiment and explore it using the mlflow user interface (ui)
Create an mlflow run and log artifacts
Create a run using a with block and view run info
Run an ml model, view info, and create runs
Run polynomial and random forest regression models -
Compare models and visualize them
Use mlflow autologging
View the autologged metrics and artifacts
Work with the conda.yaml file and other logged artifacts
Configure autologging to log test metrics
Compare mlflow models using the ui
Summarize the key concepts covered in this course
IN THIS COURSE
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1m 18sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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10m 5sLearn how to load, clean, and visualize data for machine learning. FREE ACCESS
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3. Viewing Data Statistics with Pandas Profiling6m 46sFind out how to view statistics about data with pandas profiling and use it to view correlations. FREE ACCESS
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4. Creating an MLflow Experiment8m 35sDiscover how to create an MLflow experiment and explore it using the MLflow user interface (UI). FREE ACCESS
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5. Creating an MLflow Run and Logging Artifacts9m 16sIn this video, learn how to create an MLflow run and log artifacts. FREE ACCESS
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6. Creating a Run in a With Block and Viewing Run Info5m 24sDuring this video, discover how to create a run using a with block and view run info. FREE ACCESS
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7. Creating Multiple Runs for Different Models10m 44sFind out how to run an ML model, view info, and create runs. FREE ACCESS
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8. Running Polynomial and Random Forest Regression Models10m 1sLearn how to run polynomial and random forest regression models. FREE ACCESS
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9. Comparing and Visualizing Models6m 25sDiscover how to compare models and visualize them. FREE ACCESS
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10. Using MLflow Autologging7m 47sIn this video, discover how to use MLflow autologging. FREE ACCESS
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11. Viewing Autologged Metrics and Artifacts6m 20sFind out how to view the autologged metrics and artifacts. FREE ACCESS
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12. Exploring the conda.yaml File7m 31sLearn how to work with the conda.yaml file and other logged artifacts. FREE ACCESS
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13. Configuring Autologging to Log Test Metrics4m 53sFind out how to configure autologging to log test metrics. FREE ACCESS
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14. Comparing MLflow Models Using the UI7m 57sDiscover how to compare MLflow models using the UI. FREE ACCESS
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15. Course Summary2m 22sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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