Model Management: Building & Deploying Machine Learning Models in Production

Machine Learning    |    Intermediate
  • 14 videos | 55m 41s
  • Includes Assessment
  • Earns a Badge
Rating 4.1 of 11 users Rating 4.1 of 11 users (11)
In this 14-video course, learners can explore hyperparameter tuning, versioning machine learning (ML) models, and preparing and deploying ML models in production. Begin the course by describing hyperparameter and the different types of hyperparameter tuning methods, and also learn about grid search hyperparameter tuning. Next, learn to recognize the essential aspects of a reproducible study; list ML metrics that can be used to evaluate ML algorithms; learn about the relevance of versioning ML models, and implement Git and DVC machine learning model versioning. Describe ModelDB architecture used for managing ML models, and list the essential features of the model management framework. Observe how to set up Studio.ml to manage ML models and create ML models in production, and examine Flask machine learning model setup for production. Explore how to deploy machine or deep learning models in production. The exercise involves tuning hyperparameter with grid search, versioning ML models by using Git, and creating ML models for production.

WHAT YOU WILL LEARN

  • Describe hyperparameter and the different types of hyperparameter tuning methods
    Demonstrate how to tune hyperparameters using grid search
    Recognize the essential aspects of a reproducible study
    List machine learning metrics that can be used to evaluate machine learning algorithms
    Recognize the relevance of versioning machine learning models
    Implement version control for machine learning models using git and dvc
    Describe the architecture of modeldb used for managing machine learning models
  • List essential features of the model management framework
    Set up studio.ml to manage machine learning models
    Create machine learning models in production
    Set up machine learning models in production using flask
    Deploy machine or deep learning models in production
    Tune hyperparameter with grid search, version machine learning model using git, and create machine learning models for production

IN THIS COURSE

  • 1m 25s
  • 4m 19s
    Upon completion of this video, you will be able to describe hyperparameters and the different types of hyperparameter tuning methods. FREE ACCESS
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    3.  Hyperparameter Tuning with Grid Search
    3m 28s
    In this video, you will learn how to tune hyperparameters using a grid search. FREE ACCESS
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    4.  Reproducing Study
    5m 3s
    Upon completion of this video, you will be able to recognize the essential aspects of a study that can be reproduced. FREE ACCESS
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    5.  Machine Learning Metrics
    7m 12s
    Upon completion of this video, you will be able to list machine learning metrics that can be used to evaluate machine learning algorithms. FREE ACCESS
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    6.  Machine Learning Model Versioning
    4m 56s
    Upon completion of this video, you will be able to recognize the relevance of versioning machine learning models. FREE ACCESS
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    7.  Machine Learning Model Versioning with Git and DVC
    5m 45s
    In this video, learn how to use Git and DVC for version control of machine learning models. FREE ACCESS
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    8.  ModelDB Architecture
    2m 38s
    Upon completion of this video, you will be able to describe the architecture of ModelDB used to manage machine learning models. FREE ACCESS
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    9.  Model Management Framework
    2m 25s
    Upon completion of this video, you will be able to list essential features of the model management framework. FREE ACCESS
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    10.  Studio.ml Setup
    1m 46s
    In this video, you will learn how to set up Studio.ml to manage machine learning models. FREE ACCESS
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    11.  Machine Learning Model Creation
    6m 23s
    In this video, you will learn how to deploy machine learning models in production. FREE ACCESS
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    12.  Machine Learning Model in Production
    4m 2s
    In this video, find out how to set up machine learning models for production using Flask. FREE ACCESS
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    13.  Deploying Machine Learning Model in Production
    3m
    In this video, you will deploy machine or deep learning models in production. FREE ACCESS
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    14.  Exercise: Hyperparameter Tuning and Model Versioning
    3m 20s
    Find out how to tune hyperparameters with grid search, version machine learning models using Git, and create machine learning models for production. FREE ACCESS

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