MLOps with MLflow: Hyperparameter Tuning ML Models

Mlflow 2.3.2    |    Intermediate
  • 12 videos | 1h 37m 15s
  • Includes Assessment
  • Earns a Badge
Hyperparameter tuning, an essential step to improve model performance, involves modifying a model's parameters to find the best combination for optimal results. The integration of MLflow with Databricks unlocks a powerful combination that enhances the machine learning (ML) workflow. First, you will explore the collaborative potential between MLflow and Databricks for machine learning projects. You will learn to create an Azure Databricks workspace and run MLflow models using notebooks in Databricks, establishing a robust foundation for model development in a scalable environment. Additionally, you will set up Databricks File System (DBFS) as a source of model input files. Next, you will implement hyperparameter tuning using MLflow and its integration with the hyperopt library. You will define the objective function, search space, and algorithm to optimize model performance. Through systematic tracking and comparison of hyperparameter configurations with MLflow, you will find the best-performing model setups. Finally, you will integrate SQLite with MLflow, allowing efficient management and storage of experiment-run data. You will create a regression model using scikit-learn and statsmodels, comparing the processes for the two.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline how mlflow works with databricks
    Create a databricks workspace and create a cluster to run code
    Upload a file to databricks file system (dbfs) and run a model from databricks
    Set up the objective function for hyperparameter tuning
    Review the objective function and view the runs
  • Create a search space and define a search algorithm
    Run a hyperparameter tuning model and view the results
    Set up and use sqlite to track model experiments and runs
    Perform data cleaning and build a regression model
    Build and track a regression model using statsmodel
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 45s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 10m 10s
    After completing this video, you will be able to outline how MLflow works with Databricks. FREE ACCESS
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    3.  Creating a Databricks Workspace and Cluster
    9m 26s
    Find out how to create a Databricks workspace and create a cluster to run code. FREE ACCESS
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    4.  Uploading a File to DBFS and Running a Model from Databricks
    8m 42s
    Learn how to upload a file to Databricks File System (DBFS) and run a model from Databricks. FREE ACCESS
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    5.  Setting Up the Objective Function for Hyperparameter Tuning
    10m 21s
    Discover how to set up the objective function for hyperparameter tuning. FREE ACCESS
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    6.  Understanding the Objective Function and Viewing the Runs
    10m 5s
    Upon completion of this video, you will be able to review the objective function and view the runs. FREE ACCESS
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    7.  Defining the Search Space and Search Algorithm
    9m 34s
    During this video, discover how to create a search space and define a search algorithm. FREE ACCESS
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    8.  Running a Hyperparameter Tuning Model and Viewing the Results
    6m 1s
    In this video, you will learn how to run a hyperparameter tuning model and view the results. FREE ACCESS
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    9.  Setting Up SQLite and Using MLflow with SQLite
    9m 37s
    Find out how to set up and use SQLite to track model experiments and runs. FREE ACCESS
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    10.  Performing Data Cleaning and Building a Regression Model
    7m 44s
    Learn how to perform data cleaning and build a regression model. FREE ACCESS
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    11.  Building and Tracking a Regression Model Using statsmodels
    10m 54s
    Discover how to build and track a regression model using Statsmodel. FREE ACCESS
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    12.  Course Summary
    2m 56s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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