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

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.



Expected Duration (hours)
0.9

Lesson Objectives

Model Management: Building & Deploying Machine Learning Models in Production

  • Course Overview
  • 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