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Managing Machine Learning Projecacts
  • Level Foundation
  • المدة 18 ساعات hours
  • الطبع بواسطة Duke University
  • Offered by

عن

This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems. At the conclusion of this course, you should be able to: 1) Identify opportunities to apply ML to solve problems for users 2) Apply the data science process to organize ML projects 3) Evaluate the key technology decisions to make in ML system design 4) Lead ML projects from ideation through production using best practices

الوحدات

Course Overview

    • 3 Videos
    • 2 Readings
Show info about module content

3 Videos

  • Specialization Overview
  • Instructor Introduction
  • Course Overiew

2 Readings

  • About the Course
  • Important Reminder

Identifying Opportunities

    • 3 Videos
    • 2 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Validating Product Ideas (optional)

3 Videos

  • Introduction & Objectives
  • Identifying Opportunities
  • Validating Product Ideas

2 Readings

  • Download Module Slides
  • Identifying Good Problems for ML

Do You Really Need ML?

    • 2 Videos
    • 1 Discussion
Show info about module content

1 Discussions

  • Heuristics

2 Videos

  • Benefits of ML in Products
  • ML vs. Heuristics

Review

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 1 Quiz

1 Videos

  • Module Wrap-up

Data Science Process

    • 4 Videos
    • 2 Readings
Show info about module content

4 Videos

  • Introduction and Objectives
  • ML Projects vs. Software Projects
  • CRISP-DM Data Science Process
  • CRISP-DM Case Study

2 Readings

  • Download Module Slides
  • Why are ML Projects so Hard to Manage

Project Teams

    • 3 Videos
    • 1 Discussion
Show info about module content

1 Discussions

  • Outcome and Output Metrics (optional)

3 Videos

  • Team Organization
  • Organizing the Project
  • Measuring Performance

Review

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 2 Quiz

1 Videos

  • Module Wrap-up

Sourcing Data

    • 3 Videos
    • 1 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Collecting Data (optional)

3 Videos

  • Introduction and Objectives
  • Data Needs
  • Data Collection

1 Readings

  • Download Module Slides

Data Pipelines

    • 4 Videos
    • 1 Readings
Show info about module content

4 Videos

  • Data Governence & Access
  • Data Cleaning
  • Preparing Data for Modeling
  • Reproducibility & Versioning

1 Readings

  • How We Improved Data Discovery for Data Scientists at Spotify

Review

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 3 Quiz

1 Videos

  • Module Wrap-up

ML System Design

    • 5 Videos
    • 1 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Online Prediction (optional)

5 Videos

  • Introduction and Objectives
  • ML System Design Considerations
  • Cloud vs. Edge
  • Online Learning & Inference
  • ML on Big Data

1 Readings

  • Download Module Slides

ML Tools & Technologies

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • ML Technology Selection
  • Common ML Tools

1 Readings

  • Why Jupyter is Data Science's Computational Notebook of Choice

Review

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 4 Quiz

1 Videos

  • Module Wrap-up

Challenges of Deployed Models

    • 2 Videos
    • 2 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • COVID-19 and Model Drift (optional)

2 Videos

  • Introduction and Objectives
  • ML System Failures

2 Readings

  • Download Module Slides
  • Google’s Medical AI was Super Accurate in a Lab. Real Life was a Different Story.

Managing Deployed Models

    • 4 Videos
    • 1 Plugin
Show info about module content

4 Videos

  • ML System Monitoring
  • Model Maintenance
  • Model Versioning
  • Organizational Considerations

Review

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 5 Quiz

1 Videos

  • Module Wrap-up

Course Wrap Up

    • 1 Videos
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Course Project

1 Videos

  • Course Wrap-up

Auto Summary

"Managing Machine Learning Projects" is a foundational course by Duke University's Pratt School of Engineering, offered on Coursera, focusing on the practical aspects of handling ML projects. Over 1080 minutes, you'll explore data collection, model building, deployment, and maintenance. Ideal for data science and AI enthusiasts, it helps you identify ML opportunities, organize projects using the data science process, and make key technology decisions. Subscription options include Starter and Professional. Led by expert instructors, this course is perfect for those aiming to lead ML projects from ideation to production.

Jon Reifschneider
Instructor

Jon Reifschneider