Practical Project Management for Machine Learning Projects

Name: Practical Project Management for Machine Learning Projects
Rating: 3.8 (95 reviews)

Specific Challenges and Best Practices for Managing Machine Learning Projects

Created byZorina Alliata

Last updated 6/2018

English

English [Auto],

What you'll learn

Successfully deliver Machine Learning projects.

Course content

1 section • 7 lectures • 36m total length

Course Description1:26
Learn to manage machine learning projects within the AI and big data landscape, addressing challenges, and applying best practices to deliver business value through data analytics and predictive modeling.
Introduction to Machine Learning for Project Managers6:07
Understand how machine learning uses historical data to predict outcomes and drive business goals, with supervised and unsupervised models like classification, regression, and clustering.
Specific Machine Learning Challenges: Project Initiating7:46
Guide a machine learning team by aligning agile processes with business goals, drafting a project charter with scope and data sources, and delivering targeted models that answer key business questions.
Specific Machine Learning Challenges: Project Planning10:02
Adopt an agile, CRISP-DM-based plan for machine learning projects, anchoring business and data understanding, data preparation, modeling, evaluation, and deployment with clear deliverables and epics.
Specific Machine Learning Challenges: Project Executing5:03
Navigate data access, data exploration, and iterative modeling within agile sprints, using vertical data source stories, Kanban options, and stakeholder demos to ensure verifiable, interpretable machine learning outcomes.
Specific Machine Learning Challenges: Project Monitoring3:28
Record agile metrics, such as velocity, burn down, and cumulative flow charts, then use dashboards to monitor portfolio health and measure model lift against a baseline with a control group.
Specific Machine Learning Challenges: Project Closing3:00
Close a machine learning project by defining ownership, deploying the model with a wrapper app, reading scores from a database, and establishing post deployment maintenance and change control.

Requirements

Project Management, Agile

Description

The last few years have seen a meteoric rise in disciplines related to using and exploring large quantities of data (Big Data), such as Artificial Intelligence and the Internet of Things. A part of the Artificial Intelligence domain, Machine Learning and Data Science in particular took hold in many corporations and started impacting the business outcomes. In turn, IT Project Managers are suddenly facing a different type of project they are asked to manage: the Machine Learning project.

This course is addressed to experienced IT Project Managers who want to understand how to manage Machine Learning projects, what are the specific challenges they will face, and what are some best practices to help them successfully deliver business value.

Who this course is for:

This course is addressed to experienced IT Project Managers who want to understand how to manage Machine Learning projects, what are the specific challenges they will face, and what are some best practices to help them successfully deliver business value.