MLOps with MLflow: Registering & Deploying ML Models

Mlflow 2.3.2    |    Intermediate
  • 15 videos | 1h 57m 10s
  • Includes Assessment
  • Earns a Badge
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The MLflow Model Registry enables easy registration and deployment of machine learning (ML) models for future use, either locally or in the cloud. It streamlines model management, facilitating collaboration among team members during model development and deployment. In this course, you will create classification models using the regular ML workflow. You'll see that visualizing and cleaning data, running experiments, and analyzing model performance using SHapley Additive exPlanations (SHAP) will provide valuable insights for decision-making. You'll also discover how programmatic comparison will aid in selecting the best-performing model. Next, you'll explore the powerful MLflow Models feature, enabling efficient model versioning and management. You'll learn how to modify registered model versions, work with different versions of the same model, and serve models to Representational State Transfer (REST) endpoints. Finally, you'll explore integrating MLflow with Azure Machine Learning, leveraging the cloud's power for model development.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Visualize and clean data
    Create an experiment from the mlflow user interface (ui)
    Run a classification model and view the metrics
    Analyze model insights using shapley additive explanations (shap)
    Run multiple classification models
    Compare models programmatically
    Register an mlflow model
  • Modify registered model versions and read models into python
    Register another model and view the registered model
    Serve models to a rest endpoint and access that endpoint
    Create an azure ml workspace and register a model to azure
    Deploy a model to azure and view this model
    Predict using a model's representational state transfer (rest) endpoint
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 35s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 10m 15s
    Find out how to visualize and clean data. FREE ACCESS
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    3.  Creating an Experiment from the MLflow UI
    9m 15s
    In this video, find out how to create an experiment from the MLflow user interface (UI). FREE ACCESS
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    4.  Running a Classification Model and Viewing its Metrics
    8m 10s
    Learn how to run a classification model and view the metrics. FREE ACCESS
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    5.  Analyzing Model Insights Using SHAP
    10m 37s
    During this video, discover how to analyze model insights using SHapley Additive exPlanations (SHAP). FREE ACCESS
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    6.  Running Multiple Classification Models
    7m 32s
    In this video, you will learn how to run multiple classification models. FREE ACCESS
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    7.  Comparing Models Programmatically
    10m 10s
    Discover how to compare models programmatically. FREE ACCESS
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    8.  Registering an MLflow Model
    8m 2s
    Find out how to register an MLflow model. FREE ACCESS
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    9.  Modifying Registered Model Versions
    8m 22s
    In this video, find out how to modify registered model versions and read models into Python. FREE ACCESS
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    10.  Registering Another Model and Viewing the Registered Model
    6m 48s
    Learn how to register another model and view the registered model. FREE ACCESS
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    11.  Serving Models to a Local REST Endpoint
    6m 1s
    During this video, discover how to serve models to a REST endpoint and access that endpoint. FREE ACCESS
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    12.  Creating an Azure Machine Learning (Azure ML) Account
    10m 58s
    In this video, you will learn how to create an Azure ML workspace and register a model to Azure. FREE ACCESS
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    13.  Registering a Model on Azure
    7m 58s
    Discover how to deploy a model to Azure and view this model. FREE ACCESS
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    14.  Accessing Models through Azure REST Endpoints
    8m 37s
    Learn how to predict using a model's Representational State Transfer (REST) endpoint . FREE ACCESS
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    15.  Course Summary
    2m 50s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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