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Machine Learning in Production

In this course, you will learn the best practices for managing machine learning experiments and models with MLflow. There are two main components in this course: (i) using MLflow to track the machine learning lifecycle, package models for deployment, and manage model versions (ii) examining various production issues, different deployment paradigms, and post-production concerns. By the end of this course, you will have built an end-to-end pipeline to log, deploy, and monitor machine learning models.

   
Skill Level
Professional
Duration
8h
Prerequisites
Intermediate experience with Python and pandas, Working knowledge of machine learning and data science (scikit-learn, TensorFlow, etc.), Familiarity with Apache Spark

Outline

Day 1

  • ML in production overview    
  • Data management with Delta and Databricks Feature Store    
  • Experiment tracking and versioning with MLflow Tracking    
  • Model management with MLflow Models and Model Registry    
  • Automated testing with webhooks    
  • Deployment paradigms     
  • Monitoring and CI/CD

Upcoming Public Classes

Date
Time
Language
Price
Apr 22
09 AM - 05 PM (Europe/Paris)
English
$1000.00
May 02
02 PM - 06 PM (Australia/Sydney)
English
$1000.00
May 09
01 PM - 05 PM (Asia/Tokyo)
Japanese
$1000.00
May 15
09 AM - 05 PM (Europe/Paris)
English
$1000.00
May 15
09 AM - 01 PM (America/Los_Angeles)
English
$1000.00
Jun 05
09 AM - 05 PM (Europe/London)
English
$1000.00
Jun 27
09 AM - 05 PM (Europe/Paris)
English
$1000.00
Jul 03
09 AM - 05 PM (Europe/London)
English
$1000.00
Jul 24
09 AM - 05 PM (Europe/Paris)
English
$1000.00
Jul 31
09 AM - 05 PM (Europe/London)
English
$1000.00
Aug 07
08 AM - 04 PM (America/New_York)
English
$1000.00
Aug 23
08 AM - 04 PM (America/New_York)
English
$1000.00

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