Course details

Model Management: Building & Deploying Machine Learning Models in Production

Model Management: Building & Deploying Machine Learning Models in Production


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore hyperparameter tuning, versioning machine learning models, and preparing and deploying machine learning models in production.



Expected Duration (hours)
0.9

Lesson Objectives

Model Management: Building & Deploying Machine Learning Models in Production

  • 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
  • Course Number:
    it_mlfdmmdj_02_enus

    Expertise Level
    Intermediate