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Chapter 2: Automated Machine Learning (Defined ("any machine learning…
Chapter 2: Automated Machine Learning
Life Cycle
Define Project Objectives
Acquire & Explore Data
Model Data
Interpret & Communicate
Implement, Document, & Maintain
Defined
"any machine learning system that automates repetitive tasks required for effective machine learning
automating the machine itself
Example: AirBnB taking advantage of machine learning to create a model for hosts and guests (both the individual and the aggregate)
Exploratory Data Analysis
Feature Engineering
Algorithm selection
Model diagnostics
Still areas for human decision making (such as deciding what problems need to be solved)
Tools and Platforms
example: Salesforce Einstein- runs to gather data about customers (behaviors, stats etc.)
Context Specific
General Platforms
designed for general purpose machine learning
Open source
tools are developed by and for computer and data scientists, generally require knowledge of programming languages
Commercial
provided by commercial vendor, for a price (require coding skills)
Eight Criteria for AutoML Excellence
productivity
accuracy
most important
ease of use
understanding and learning
resource availability
process transparency
generalization
recommend actions