This document summarizes the learnings and evolution of EZML, a proposed machine learning tool. It began by targeting data scientists, but interviews revealed that feature extraction was more important than automated model selection. The target then became companies lacking data science capabilities. Further interviews identified the ideal customer as a startup CTO with a recommendation or engagement problem. An MVP was developed with tiered pricing and consulting. Ongoing challenges around data privacy and costs were noted. The document concludes by questioning the business viability and next steps.

2. eZmLThe Old The New
An ecosystem for machine learning. EZML
will allow anyone of any background to
upload data and receive a solution for their
machine learning problem.
Size of Opportunity:
SAM: 10 Billion (startups 10~100 ees)
Target Market: 8 Billion (high tech)
Helping startups increase user engagement and
retention through crowdsourced
recommendations and analytics. Engineers
without any data science background can
upload data and seamlessly integrate a
machine learning solution into their product.
Size of Opportunity:
SAM: 10 Billion (startups wz 10~100 ees)
Target Market: 4 Billion (consumer internet)
# of interviews: 103

3. THE TEAM
Jim Cai
MSCS
Technical Lead LinkedIn
Engineering
William Song
MSCS
Self driving Car
Google[x]; Facebook
Engineering
Billy Jun
MSCS
Robotics at MDA
Engineering
Sam Yu
MSx GSB
SAIF Partners, GE
Strategy + Bizdev
Jordan Segall
MS in MS&E, BSCS
PM RelateIQ; FDE Palantir
Product + Bizdev

4. AT THE BEGINNINg:
ORIGINAL HYPOTHESES
WHAT: Model selection is time consuming and can be
automated
WHO: Data scientists within companies that can automate
“annoying” parts of their job
HOW: We can provide the entire model to the user that can
be embedded within their system at the end of the process
WHAT DID WE DO?
Talked to primarily data scientists at tech companies and
data science consulting

5. Business Model
Canvas #1:
1/6/15
Where are the
data scientists at
companies?

6. LESSONS LEARNED: PART 1
PREVIOUSLY
Model selection is time consuming and
can be automated

7. Automated Model
Recommendation
Data Scientist Model
Contribution
On-the-fly update
API Access
Saves time performing model
selection and money hiring data
scientists
Obtain models that improve user
engagement and
product quality
Purchased model continues to
improve automatically
Automated model selection system produces
the best model with little to no data science
knowledge
Save the customers time and money by
running everything in
the cloud

8. “Typically less than 20% of our time in data
science consulting is model selection. We’ll get
it to working ‘decently enough’ then move
on...we don’t aim for the best...we would ONLY
use EZML if the feature extraction part of the
project is trivial. Feature extraction is critically
important.”
- Jay Hack, Founder 205 Consulting, Palantir
Engineer

9. “Model selection is the easy problem; feature
extraction is the hard part.”
- Nick Gorski, Machine Learning Lead at
TellPart

10. LESSONS LEARNED: PART 1
HYPOTHESIS:
Data Scientists may be the first target
customer to go for

11. Photozeen - one of the first companies we
interviewed that recognized their deficiency in
machine learning, and needed help to improve
user retention and engagement.

12. LESSONS LEARNED: PART 1
HYPOTHESIS:
We can pitch the product

13. “We don’t understand what the **** you guys
are doing.”
- Steve Weinstein

14. GOING FORWARD
Is there a need?
Who is our customer
archetype?
Interview more companies
Develop MVP
Hone pitch
Articulate product more clearly

15. UPDATED HYPOTHESES
WHAT: Our tool will still provide machine learning as a
service, but our target customer are companies, not data
scientists/consulting firms.
WHO: Anyone within the firm, from engineers to marketing,
can use our tool. Our target contact is the founder.

17. Reached out to 200+ companies
● 51 from Y-Combinator
● 49 from Crunchbase
● 35 from Tec Club
● 34 from Tech Stars
● and more
WHat we did

18. LESSONS LEARNED: PART 2
There is a need for companies lacking
data science solutions to use our tool

20. While these companies ultimately
did not work out….
….the reasons why they didn’t
were key to our learning

21. RelateIQ
Too large with enough data scientists on board;
would need fully working EZML
Verge Campus
Recommendation is an ancillary feature
Photozeen
Good problem for us, but not enough data

22. LESSONS LEARNED: PART 2
Privacy would not be a concern for
companies that use our tool; they
would reveal their data features publicly
for better results, Kaggle style.

23. “You need to properly address privacy and
security policies before we share data...we
don’t want to reveal our secret sauce.”
Olga Mack, Assistant General Counsel, Zoosk
“I’m uncomfortable sending you my data since our business is based on selling data - privacy is
already a touchy subject for us.”
Andy Bromberg, CEO Sidewire

24. LESSONS LEARNED: PART 2
Our customer archetype are CEOs and
CTOs

25. We would go to CEOs, who would redirect us
to their CTOs.
CTOs tended to have a greater understanding
of the value add to their business, and how
they were unable to implement machine
learning without a tool like EZML.

26. CUSTOME ARCHETYPE
Startup with <10M in funding
CTO or head data scientist/engineer
enough data for machine learning to have effect
no intense privacy concerns
clear problems (ie. user engagement; user churn)

27. BUSINESS MODEL CANVAS
Business Model
Canvas: 2/10/15

28. UPDATED HYPOTHESES
SELF-SERVICE: Since it is a self-service tool, the
sales process won’t be difficult
CONSULTING: We can apply a consulting model
for early customers; in exchange for manually
working on problems, we can learn from real
data and create our platform

29. NEW MVP

31. Keep Customers
ACTIVATE
ACQUIRE
UP-SELL
NEXT-SELL
CROSS-SELL
REFERRALS
Perfect User Experience:
Easiness, Accuracy
Automatic model upgrade
with continual data upload
Free access to machine learning
models for analytics
Low charge for API calls
Focused sectors (consumer internet)
and algorithms (recommendation)
Expand features / algorithms to tackle
new problems (Upsell)
Invest in sales force, data scientist
models and training data sets
Provide consulting and customization

32. DIAGRAM OF PAYMENT MODELS:
Amazon EC2
(model
parameters)
eZmL
Compute/storage
resources
Amazon S3
(dataset storage)
- CPU server
$14.17/month/company
- GPU server
$25.73/month/company
$0.0295/GB/mo
Data Scientist
Models
Compensation
%/selected
model
Upload data
($0.030/GB/month)
Evaluation Report
Subscribe
($ 200/model/month)
API Call
Prediction
Continual Improvement of
Model
($ 50/model/month)
Startups
Free Bandwidth
Train model (variable
cost after 10 iterations)

34. Hive

35. SO WHAT NOW?

36. WE LEARNED...
What type of
company to target
Who in the
company to sell to
We need a highly
trained sales team
Provide consulting
for early clients to
get data
Tier pricing is
better than buying
model or charge
per API call
Companies are
most interested in
recommendation
types of problems
Crowdsourcing is
a second priority
at the moment
The costs of
storage, servers,
employees, office
space, and more
We can reduce
costs (user churn),
not just revenue
increases
Feature extraction
is key differentiator
Privacy is a
concern we must
tend to
Dumping of data
for continued
training is highly
important
And many many more

37. WE THINK...
Is this a viable business?
Do we want to pursue it after this class?

43. BUSINESS MODEL CANVAS

44. BUSINESS CANVAS

45. BUSINESS CANVAS

47. THANK YOU
Steve Blank, Jeff Epstein, Steve Weinstein
Nick O’Connor + Sunil Nagaraj
Classmates + TAs