The Machine Learning Canvas is a template for developing new (or documenting existing) intelligent systems based on data and machine learning. It is a visual chart with elements describing the key aspects of such systems: the value proposition, the data to learn from (to create predictive models), the utilization of predictions (to create proposed value), requirements and measures of performance. It assists teams of data scientists, software engineers, product and business managers, in aligning their activities.
This tutorial will help you get into the right mindset to go beyond the current hype around machine learning, beyond proofs of concept, and to clearly see how this technology can have an actual impact in your domain. I’ll present the general structure of the Canvas, the different boxes it is composed of and the associated questions to answer. We’ll see how to fill it in iteratively on a churn prevention example.
21. 1. Show churn rate against time
2. Predict which customers will churn next
3. Suggest what to do about each customer
(e.g. propose to switch plan, send promotional offer, etc.)
21
Churn analysis
22. • Who: SaaS company selling monthly subscription
• Question asked:“Is this customer going to leave within 1
month?”
• Input: customer
• Output: no-churn or churn
• Data collection: history up until 1 month ago
22
Churn prediction
23. Assume we know who’s going to churn. What do we do?
• Contact them (in which order?)
• Switch to different plan
• Give special offer
• No action?
23
Churn prediction prevention
24. “3. Suggest what to do about each customer”
→ prioritised list of actions, based on…
• Customer representation
• Churn prediction
• Prediction confidence
• Revenue brought by customer
• Constraints on frequency of solicitations
24
Churn prevention
25. • Taking action for each TP (and FP) has a cost
• For each TP we“gain”: (success rate of action) *
(revenue /cust. /month)
• Imagine…
• perfect predictions
• revenue /cust. /month = 10€
• success rate of action = 20%
• cost of action = 2€
• What is the ROI?
25
Churn prevention ROI
28. 28
The Machine Learning Canvas
The Machine Learning Canvas (v0.4) Designed for: Designed by: Date: Iteration: .
Decisions
How are predictions used to
make decisions that provide
the proposed value to the enduser?
ML task
Input, output to predict,
type of problem.
Value
Propositions
What are we trying to do for the
enduser(s) of the predictive system?
What objectives are we serving?
Data Sources
Which raw data sources can
we use (internal and
external)?
Collecting Data
How do we get new data to
learn from (inputs and
outputs)?
Making
Predictions
When do we make predictions on new
inputs? How long do we have to
featurize a new input and make a
prediction?
Offline
Evaluation
Methods and metrics to evaluate the
system before deployment.
Features
Input representations
extracted from raw data
sources.
Building Models
When do we create/update
models with new training
data? How long do we have to
featurize training inputs and create a
model?
Live Evaluation and
Monitoring
Methods and metrics to evaluate the
system after deployment, and to
quantify value creation.
machinelearningcanvas.com by Louis Dorard, Ph.D. Licensed under a Creative Commons AttributionShareAlike 4.0 International License.
29. • (Not an adaptation of the Business Model Canvas)
• Describe the Learning part of a predictive system / an intelligent
application:
• What data are we learning from?
• How are we using predictions powered by that learning?
• How are we making sure that the whole thing“works”through
time?
29
The Machine Learning Canvas
31. –Ingolf Mollat, Principal Consultant at Blue Yonder
“The Machine Learning Canvas is providing our
clients real business value by supplying the first
critical entry point for their implementation
of predictive applications.”
45. • Assist data scientists, software engineers, product and business
managers, in aligning their activities
• Make sure all efforts are directed at solving the right problem!
• Choose right algorithm / infrastructure / ML solution prior to
implementation
• Guide project management
• machinelearningcanvas.com
45
Why fill in ML canvas?
46.
47. –Jeremy Howard
“Great predictive modeling is an important
part of the solution, but it no longer stands on its
own; as products become more sophisticated, it
disappears into the plumbing.”