Machine Learning Isn’t Magic
From the Voices on Project Management Blog
by Cameron McGaughy,
Lynda Bourne, Kevin Korterud, Conrado Morlan, Peter Tarhanidis, Mario Trentim, Jen Skrabak, David Wakeman, Wanda Curlee, Christian Bisson, Ramiro Rodrigues, Soma Bhattacharya, Emily Luijbregts, Sree Rao, Yasmina Khelifi, Marat Oyvetsky, Lenka Pincot, Jorge Martin Valdes Garciatorres, cyndee miller
Voices on Project Management offers insights, tips, advice and personal stories from project managers in different regions and industries. The goal is to get you thinking, and spark a discussion. So, if you read something that you agree with--or even disagree with--leave a comment.
View Posts By:
Cameron McGaughy
Lynda Bourne
Kevin Korterud
Conrado Morlan
Peter Tarhanidis
Mario Trentim
Jen Skrabak
David Wakeman
Wanda Curlee
Christian Bisson
Ramiro Rodrigues
Soma Bhattacharya
Emily Luijbregts
Sree Rao
Yasmina Khelifi
Marat Oyvetsky
Lenka Pincot
Jorge Martin Valdes Garciatorres
cyndee miller
Past Contributors:
Rex Holmlin
Vivek Prakash
Dan Goldfischer
Linda Agyapong
Jim De Piante
Siti Hajar Abdul Hamid
Bernadine Douglas
Michael Hatfield
Deanna Landers
Kelley Hunsberger
Taralyn Frasqueri-Molina
Alfonso Bucero Torres
Marian Haus
Shobhna Raghupathy
Peter Taylor
Joanna Newman
Saira Karim
Jess Tayel
Lung-Hung Chou
Rebecca Braglio
Roberto Toledo
Geoff Mattie
Recent Posts
Project 2030: Skills We Need to Cultivate Now
The Technical Program Manager: How to Stay Relevant in 2025
5 Things Your Operational Plan Should Do
5 New Project Guardrails for Adaptive Leaders
The Leader's Voice: Respect It, Protect It, and Use It Properly!
Categories
2020,
Adult Development,
Agile,
Agile,
Agile,
agile,
Agile management,
Agile management,
Agile;Community;Talent management,
Artificial Intelligence,
Backlog,
Basics,
Benefits Realization,
Best Practices,
BIM,
business acumen,
Business Analysis,
Business Analysis,
Business Case,
Business Intelligence,
Business Transformation,
Calculating Project Value,
Canvas,
Career Development,
Career Development,
Career Help,
Career Help,
Career Help,
Career Help,
Careers,
Careers,
Careers,
Careers,
Categories: Career Help,
Change Management,
Cloud Computing,
Collaboration,
Collaboration,
Collaboration,
Collaboration,
Collaboration,
Communication,
Communication,
Communication,
Communication,
Communications Management,
Complexity,
Conflict,
Conflict Management,
Consulting,
Continuous Learning,
Continuous Learning,
Continuous Learning,
Continuous Learning,
Continuous Learning,
Cost Management,
COVID-19,
Crises,
Crisis Management,
critical success factors,
Cultural Awareness,
Culture,
Decision Making,
Design Thinking,
Digital Project Management,
Digital Transformation,
digital transformation,
Digitalisation,
Disruption,
Diversity,
Diversity,
Documentation,
Earned Value Management,
Education,
EEWH,
Enterprise Risk Management,
Escalation management,
Estimating,
Ethics,
execution,
Expectations Management,
Facilitation,
feasibility studies,
Future,
Future of Project Management,
Generational PM,
Governance,
Government,
green building,
Growth,
Horizontal Development,
Human Aspects of PM,
Human Aspects of PM,
Human Aspects of PM,
Human Aspects of PM,
Human Aspects of PM,
Human Resources,
Inclusion,
Information Technology,
Innovation,
Intelligent Building,
International,
International Development,
Internet of Things (IOT),
Internet of Things (IoT),
IOT,
Knowledge,
Leadership,
Leadership,
Leadership,
Leadership,
Leadership,
lean construction,
LEED,
Lessons Learned,
Lessons learned;Retrospective,
Managing for Stakeholders,
managing stakeholders as clients,
Mentoring,
Mentoring,
Mentoring,
Mentoring,
Mentoring,
Methodology,
Metrics,
Micromanagement,
Microsoft Project PPM,
Motivation,
Negotiation,
Neuroscience,
neuroscience,
New Practitioners,
Nontraditional Project Management,
OKR,
Online Learning,
opportunity,
Organizational Culture,
Organizational Project Management,
Pandemic,
People management,
Planing,
planning,
PM & the Economy,
PM History,
PM Think About It,
PMBOK Guide,
PMI,
PMI EMEA 2018,
PMI EMEA Congress 2017,
PMI EMEA Congress 2019,
PMI Global Conference 2017,
PMI Global Conference 2018,
PMI Global Conference 2019,
PMI Global Congress 2010 - North America,
PMI Global Congress 2011 - EMEA,
PMI Global Congress 2011 - North America,
PMI Global Congress 2012 - EMEA,
PMI Global Congress 2012 - North America,
PMI Global Congress 2013 - EMEA,
PMI Global Congress 2013 - North America,
PMI Global Congress 2014 - EMEA,
PMI Global Congress 2014 - North America,
PMI GLobal Congress EMEA 2018,
PMI PMO Symposium 2012,
PMI PMO Symposium 2013,
PMI PMO Symposium 2015,
PMI PMO Symposium 2016,
PMI PMO Symposium 2017,
PMI PMO Symposium 2018,
PMI Pulse of the Profession,
PMO,
PMO,
pmo,
PMO Project Management Office,
portfolio,
Portfolio Management,
Portfolio Management,
portfolio management,
presentations,
Priorities,
Probability,
Problem Structuring Methods,
Process,
Procurement Management,
profess,
Program Management,
project,
Project Delivery,
Project Dependencies,
Project Failure,
project failure,
Project Leadership,
Project Management,
project management,
project management office,
Project Planning,
project planning,
Project Requirements,
Project Success,
Ransomware,
Reflections on the PM Life,
Remote,
Remote Work,
Requirements Management,
Research Conference 2010,
Researching the Value of Project Management,
Resiliency,
Risk Management,
Risk Management,
Risk management,
risk management,
ROI,
Roundtable,
Salary Survey,
Schedule Management,
Scheduling,
Scope Management,
Scrum,
search,
SelfLeadership,
SelfLeadership,
SelfLeadership,
SelfLeadership,
SelfLeadership,
Servant Leadership,
Sharing Knowledge,
Sharing Knowledge,
Sharing Knowledge,
Sharing Knowledge,
Sharing Knowledge,
Social Responsibility,
Sponsorship,
Stakeholder Management,
Stakeholder Management,
stakeholder management,
Strategy,
Strategy,
swot,
Talent Management,
Talent Management,
Talent Management,
Talent Management,
Talent Management,
Talent Management Leadership SelfLeadership Collaboration Communication,
Taskforce,
Teams,
Teams in Agile,
Teams in Agile,
teamwork,
Tech,
Technical Debt,
Technology,
TED Talks,
The Project Economy,
Timeline,
Tools,
tools,
Transformation,
transformation,
Transition,
Trust,
Value,
Vertical Development,
Volunteering,
Volunteering #Leadership #SelfLeadership,
Volunteering Sharing Knowledge Leadership SelfLeadership Collaboration Trust,
VUCA,
Women in PM,
Women in Project Management
Date
By Christian Bisson, PMP
Machine learning is one of today’s hottest tech topics.
It’s essentially a type of artificial intelligence (AI) in which you give your software the ability to “learn” based on data. For example, you probably notice how YouTube, Netflix, Amazon and many other companies suggests videos or products you should check out. These suggestions are based on your previous online actions, or those of other people deemed “similar” to you.
For some time now I’ve been working on projects that involve this technology. We often have clients who want machine learning even though they do not know if it’s even relevant to them. Since “everyone is doing it,” they want to do it too.
Calibrating a project sponsor’s expectations is often a good idea. While the automated services generated through machine learning may seem magical, getting to that point involves challenges—and a lot of work.
1. It needs quality data.
The machine will learn using the data it has being given—that data is the crucial starting point. The data that’s available is what drives how the machine will evolve and what added value machine learning can bring to your project/product. For example, if you are trying to teach the machine to recognize vehicles on images it scans, and all you can teach it with are images of small cars, you are not set up for success. You need a better variety of images.
The machine’s ability to learn is directly tied to the quality of the data it encounters.
2. It needs lots of data.
Once you have quality data, you need it in high quantities. If you can only provide the machine with the website behaviors of, say, hundreds of users per month, don’t expect it to have enough information to be able to recommend the best products based on user trends. Its sample will be too little to be able to be accurate.
3. It needs to be tested continually.
Once you have the necessary data, the journey is not over. The machine may learn on its own, but it’s learning based on how it was built and with the data it’s being fed. There is always room for improvement.
4. It’s costly.
As amazing as machine learning is, it is not cheap. So keep an eye on your project’s budget. Machine learning experts can command high salaries, and there is a lot of effort involved with researching the best approach—creating the models, training them, testing them, etc. Make sure the ROI is worth it.
Have you had a chance to work on a project involving machine learning? What challenges have you faced?
Posted
by
Christian Bisson
on: July 14, 2018 08:59 AM |
Permalink
Comments (12)
Please login or join to subscribe to this item
Quality in, quality out. Data information knowledge is such a finely balanced process to get value from it. Thanks Christian.
Drew Craig
Sr. Agile & Product Coach| Vanguard
Philadelphia, Pa, United States
Thanks, Christian. Absolutely.
Dawson Preethi
Design Manager BSc(Eng) MSc (Str Eng) MASCE PMP| AECOM ARABIA
Riyadh, Saudi Arabia
Quite a concise introduction! thanks for sharing.
Al Chen
Solutions Consultant| Coda
New York, Ny, United States
I don't think it necessarily needs to be costly. You can get some good feedback about your dataset and possibly find some good data science candidates by posting your project to a community like kaggle.com. The downside is that you have to be open to sharing your data with the community which may contain sensitive info.
Good prelude on machine learning. Thanks for sharing.
Thank you. The content gives a better insight .
Thanks everyone for your feedback.
Al chen, I agree that their might be ways to reduce the costs, but by experience, reducing costs generally comes with less quality or other constraints (ie. sharing data like you mentioned) so it is something to keep in mind if it's a deal breaker or not for the project!
Cheers.
RAJESH K L
Project Manager, PMP| Bharat Electronics, Bengaluru, India
Bengaluru, Karnataka, India
Adam Bakos
Lead Airworthiness, Global 5/6 New Product Development| Bombardier Aviation
Mississauga, Ontario, Canada
There was a good presentation on AI / Machine Learning at the Dubai International Project Management Forum yesterday, and it is true that if you don't rely on the cloud to mutualize LOADS of data, what you can achieve with it is quite limited...
Stephen Alfano
Executive Consultant| KAI Partners, Inc.
El Dorado Hills, Ca, United States
Thanks for sharing, Christian,
Please Login/Register to leave a comment.
|
"There is not one wise man in 20 that will praise himself."
- William Shakespeare
|
