MLOps with MLflow: Creating & Tracking ML Models

Mlflow 2.3.2    |    Intermediate
  • 15 videos | 1h 45m 24s
  • Includes Assessment
  • Earns a Badge
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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

  • 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

  • 1m 18s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 10m 5s
    Learn how to load, clean, and visualize data for machine learning. FREE ACCESS
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    3.  Viewing Data Statistics with Pandas Profiling
    6m 46s
    Find 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 Experiment
    8m 35s
    Discover 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 Artifacts
    9m 16s
    In 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 Info
    5m 24s
    During 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 Models
    10m 44s
    Find 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 Models
    10m 1s
    Learn how to run polynomial and random forest regression models. FREE ACCESS
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    9.  Comparing and Visualizing Models
    6m 25s
    Discover how to compare models and visualize them. FREE ACCESS
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    10.  Using MLflow Autologging
    7m 47s
    In this video, discover how to use MLflow autologging. FREE ACCESS
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    11.  Viewing Autologged Metrics and Artifacts
    6m 20s
    Find out how to view the autologged metrics and artifacts. FREE ACCESS
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    12.  Exploring the conda.yaml File
    7m 31s
    Learn how to work with the conda.yaml file and other logged artifacts. FREE ACCESS
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    13.  Configuring Autologging to Log Test Metrics
    4m 53s
    Find out how to configure autologging to log test metrics. FREE ACCESS
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    14.  Comparing MLflow Models Using the UI
    7m 57s
    Discover how to compare MLflow models using the UI. FREE ACCESS
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    15.  Course Summary
    2m 22s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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