MLOps with MLflow: Using MLflow Projects & Recipes

Mlflow 2.3.2    |    Expert
  • 17 videos | 2h 8m 23s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
MLflow Projects enable you to package machine learning code, data, and environment specifications for reproducibility and easy sharing. Registering projects in MLflow simplifies version control and enhances collaboration within data science teams. MLflow Recipes, on the other hand, automate and standardize machine learning tasks with pre-defined templates and configurations, promoting consistency and repeatability while allowing customization for specific applications. With recipes and projects combined, MLflow becomes a powerful tool for impactful and consistent results, streamlining data science workflows. You will start this course by learning how MLflow Projects enable you to package, share, and reproduce machine learning code. Next, you will learn about MLflow Recipes that automate machine learning tasks in reproducible environments. You will explore the MLflow Regression Template, customize its files for model training, and run the recipe to view the model's performance. Finally, you will explore running a classification recipe in Databricks and modifying YAML and code files for configuration.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline how to work with mlflow projects
    Create an mlflow project and view and modify project files
    Create an experiment for a project, run it, and view results
    Outline the use of mlflow recipes
    Create an mlflow recipe and explore the files in it
    View the mlflow regression template, clone it, and use it
    View files in a recipe and modify those files
    Modify the train.py file in a recipe and modify the custom_metrics.py file
  • Work with the recipe.yaml file and the local.yaml
    Create a recipe and view the recipe pipeline
    Run a recipe and view the evaluation of the model
    Validate models based on a metrics threshold
    Set up a classification recipe and modify the yaml files
    Run a classification recipe and view the result
    Train using data from databricks file system (dbfs) and delta lakes
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 41s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 4m 15s
    Upon completion of this video, you will be able to outline how to work with MLflow Projects. FREE ACCESS
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    3.  Creating, Viewing, and Modifying an MLflow Project
    10m 57s
    In this video, find out how to create an MLflow project and view and modify project files. FREE ACCESS
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    4.  Creating and Running an Experiment for a Project and Viewing Results
    9m 54s
    During this video, discover how to create an experiment for a project, run it, and view results. FREE ACCESS
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    5.  MLflow Recipes
    4m 40s
    During this video, you will learn how to outline the use of MLflow Recipes. FREE ACCESS
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    6.  Creating an MLflow Recipe and Exploring Its Files
    6m 51s
    Find out how to create an MLflow recipe and explore the files in it. FREE ACCESS
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    7.  Using the MLflow Regression Template
    5m 54s
    In this video, you will learn how to view the MLflow regression template, clone it, and use it. FREE ACCESS
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    8.  Viewing and Modifying Files in a Recipe
    9m 32s
    Discover how to view files in a recipe and modify those files. FREE ACCESS
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    9.  Modifying the train.py and the custom_metrics.py File
    7m 26s
    In this video, you will learn how to modify the train.py file in a recipe and modify the custom_metrics.py file. FREE ACCESS
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    10.  Working with the recipe.yaml and local.yaml Files
    8m 21s
    In this video, find out how to work with the recipe.yaml file and the local.yaml. FREE ACCESS
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    11.  Creating a Recipe and Viewing the Recipe Pipeline
    8m 48s
    During this video, discover how to create a recipe and view the recipe pipeline. FREE ACCESS
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    12.  Running Our Recipe and Viewing Model Evaluation Results
    7m 34s
    During this video, you will learn how to run a recipe and view the evaluation of the model. FREE ACCESS
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    13.  Validating Models Based on a Metrics Threshold
    10m 56s
    Find out how to validate models based on a metrics threshold. FREE ACCESS
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    14.  Setting up a Classification Recipe and Modifying the YAML Files
    10m 10s
    In this video, you will learn how to set up a classification recipe and modify the yaml files. FREE ACCESS
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    15.  Running a Classification Recipe and Viewing the Results
    8m 33s
    Discover how to run a classification recipe and view the result. FREE ACCESS
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    16.  Training Models with Data from DBFS and Delta Lakes
    10m 10s
    In this video, you will learn how to train using data from Databricks File System (DBFS) and Delta Lakes. FREE ACCESS
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    17.  Course Summary
    2m 43s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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