Model Management: Building & Deploying Machine Learning Models in Production

Machine Learning    |    Intermediate
  • 14 Videos | 1h 1m 41s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Course Overview
    1m 25s
    UP NEXT
  • Playable
    2. 
    Hyperparameter Tuning
    4m 19s
  • Locked
    3. 
    Hyperparameter Tuning with Grid Search
    3m 28s
  • Locked
    4. 
    Reproducing Study
    5m 3s
  • Locked
    5. 
    Machine Learning Metrics
    7m 12s
  • Locked
    6. 
    Machine Learning Model Versioning
    4m 56s
  • Locked
    7. 
    Machine Learning Model Versioning with Git and DVC
    5m 45s
  • Locked
    8. 
    ModelDB Architecture
    2m 38s
  • Locked
    9. 
    Model Management Framework
    2m 25s
  • Locked
    10. 
    Studio.ml Setup
    1m 46s
  • Locked
    11. 
    Machine Learning Model Creation
    6m 23s
  • Locked
    12. 
    Machine Learning Model in Production
    4m 2s
  • Locked
    13. 
    Deploying Machine Learning Model in Production
    3m
  • Locked
    14. 
    Exercise: Hyperparameter Tuning and Model Versioning
    3m 20s

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