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 courseLoad, clean, and visualize data for machine learningView statistics about data with pandas profiling and use it to view correlationsCreate an mlflow experiment and explore it using the mlflow user interface (ui)Create an mlflow run and log artifactsCreate a run using a with block and view run infoRun an ml model, view info, and create runsRun polynomial and random forest regression models
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Compare models and visualize themUse mlflow autologgingView the autologged metrics and artifactsWork with the conda.yaml file and other logged artifactsConfigure autologging to log test metricsCompare mlflow models using the uiSummarize 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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6m 46sFind out how to view statistics about data with pandas profiling and use it to view correlations. FREE ACCESS
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8m 35sDiscover how to create an MLflow experiment and explore it using the MLflow user interface (UI). FREE ACCESS
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9m 16sIn this video, learn how to create an MLflow run and log artifacts. FREE ACCESS
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5m 24sDuring this video, discover how to create a run using a with block and view run info. FREE ACCESS
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10m 44sFind out how to run an ML model, view info, and create runs. FREE ACCESS
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10m 1sLearn how to run polynomial and random forest regression models. FREE ACCESS
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6m 25sDiscover how to compare models and visualize them. FREE ACCESS
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7m 47sIn this video, discover how to use MLflow autologging. FREE ACCESS
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6m 20sFind out how to view the autologged metrics and artifacts. FREE ACCESS
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7m 31sLearn how to work with the conda.yaml file and other logged artifacts. FREE ACCESS
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4m 53sFind out how to configure autologging to log test metrics. FREE ACCESS
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7m 57sDiscover how to compare MLflow models using the UI. FREE ACCESS
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2m 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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