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Project Management
in AI
Nathanael Weill, PhD
About me
2
Master in Bioinformatics Strasbourg University (France)
Ph.D. In Pharmaceutical Science. Strasbourg University (France)
Post-Doc at McGill (Computational chemistry)
Post-Doc at UdeM (Computational Biology)
Senior Data Scientist at Mnubo (IoT company)
Nathanael Weill
What is AI?
Why AI?
AI project phases
Warnings
Optimize the process
Outlines
3
The theory and development of computer systems able to perform
tasks that normally require human intelligence, such as visual perception,
speech recognition, decision-making, and translation between languages. (google
dictionary)
What is AI?
4
Prediction: The process of filling in
missing information. Prediction takes data
you have to generate data you don’t have.
How does it work?
5
computer
Input data
Output
Function
computer
Input data
Function
Output
computer
New Input data
Prediction
Function
Why AI?
6
Prediction became cheaper
Data
AccuracyClient
The AI race
7
Symposium 2019 : Gestion de projet en Intelligence Artificielle
Big Data & Data Science Projects
Failure Rate
9
GARTNER
ESTIMATED
85%
of big data projects
fail (2017). The
initial estimation
was 60% (GARTNER
2016)
THROUGH 2020
80%
of AI projects will
remain alchemy,
run by wizards
whose talents will
not scale in the
organization.
(GARTNER 2018)
THROUGH 2022
20%
of analytic insights
will deliver
business
outcomes. (GARTNER
2018)
EXECUTIVE
SURVEY
77%
respondents say
that “business
adoption” of big
data and AI
initiatives
continues to
represent a
challenge for their
organizations
(NEWVANTAGE
PARTNERS 2019)
A recipe for failure
We must define the solution as an entire process.
If prediction is the end of the solution, the entire solution might fail because:
• The output does not correspond to the operational needs.
• The operator will not use it due to complexification of the process.
• No one is capable of managing the algorithms if something goes wrong.
• …
Data Algorithm Prediction
Data Algorithm Prediction
Judgment
Action
Feedback
Critical! We have to make sure we produce the right
information and in the right format to help the person in charge
to take action
Manager: Person in charge to take action. We need to
make sure this person is involved early in the process
Design of the solution
Identification of
the problem to
solve
Design the
appropriate
solution
Proof of concept
Productization
Scale the process
Reorganize the
company
6 Phases
12
Use Case
13
At Mnubo we designed a 3-5 days workshop
with clients to go from the problem identification
to the mock up of the solution
Performance problem? Scalability issue?
How to Consume the predictions? Maintain the solution?
What action(s) will be taken?
…
Ex:
1 prediction per machine? Every hour? 12 hours?
Solving the right problem
14
A journey as a Data Scientist 1/2
15
Data Scientist:
Define the valuable business
problem
Translate the business problem
into a KPI
A Key Performance Indicator (KPI) is a
measurable value that demonstrates how
effectively a company is achieving key
business objectives. Organizations use key
performance indicators at multiple levels to
evaluate their success at reaching targets.
Client:
« I loose a lot of money when the
assembly lines stops ».
« I would like to reduce the number of
machine failures ».
https://www.klipfolio.com/resources/kpi-examples
A journey as a Data Scientist 2/2
16
Data Scientist:
Define the metric and the
definition of success.
Next phase: Proof of concept.
• explore
• Establish a baseline
• Iterate!!!
Client:
A success would be to predict
failure 12 hours in advance
with an accuracy of 80%
According to the final report, I
get an answer to:
• Is the objectives reasonable?
• How should I productize the
solution?
POC: Critical choice
17
Time
Resources Accuracy
• Explore
• Create a baseline
• Iterate
Agile
Productization phase
18
2 productization models:
• Data scientist write specifications and engineers take over and
rewrite the code in an other language (java, scala…)
• Data scientist with a team of data engineer, dev ops etc… takes
the code written and deploy it in the infrastructure
Pros and cons…
Data Algorithm Prediction
Judgment
Action
Feedback
Data Algorithm Prediction
Judgment
Action
Feedback
Data Algorithm Prediction
Judgment
Action
Feedback
Data Algorithm Prediction
Judgment
Action
Feedback
Data Algorithm Prediction
Judgment
Action
Feedback
Data Algorithm Prediction
Judgment
Action
Feedback
Data Algorithm Prediction
Judgment
Action
Feedback
Full solution management:
• Configuration
• Monitoring
• ROI evaluation
Scaling of the Solution
Avoid silos
labyrinthine system
Data Algorithm
Judgment
Action
Feedback
Data Algorithm Prediction
Judgment
Action
Feedback
(Automating)
Dev ops: In charge of deploying and maintaining the
infrastructure to support the solution
Data engineer: in charge of setting the appropriate
resource to access the data.
Data scientist: in charge of creating the machine
learning model (pipeline data to prediction)
Roles: development phases
21
Operator: In charge of activating/deactivating the
algorithms designed for specific predictions/actions
=> Provide feedback to data scientists
Data scientist: Integrate the feedback and update the
algorithm (if needed)
Dev ops: Maintain the infrastructure
Roles: long term
22
Company perturbation
23
IT Team
Operation
Team
Executives
Data Science
Team
24
The Proof of Concept Curse in AI and IoT
80% of companies stop at
the POC stage.
Laggards & Winners
I recommend:
To use Agile methodology in all
phases of the project
Have a clear understanding of
the final aim in term of:
• The process of development
• The perturbation of the company
organization
Critical role of the project manager
25
Phases:
Identification of the problem to
solve
Design the appropriate solution
Proof of concept
Productization
Scale the process
Reorganize the company
There is multiple tracks that can be done in parallel:
Data acquisition
To make sure the data are available in (near-) real time.
Creation of the machine learning algorithm
Create the appropriate pipeline to train, test and deploy the model(s).
Creation of the end point to expose the predictions
A dashboard, an app, an alerting system, a reporting system.
Monitoring of the pipeline
monitor the data acquisition, the performance of the model, the use of the
end point…
Process to capture the action taken and consolidate a feedback
loop
Optimize the process
26
Hofstadter's law: It always takes longer than you expect, even when
you take into account Hofstadter's Law.
First AI project is hard, you should start with an easy project
• Is there already a system in place to monitor the KPI?
• Is the data pipeline already in place?
• Is AI a replacement for an existing system?
Assess the client maturity is hard especially regarding the company
perturbation
A good PM is the key to success!
Wrap up
27
Nathanael Weill
nweill@mnubo.com
28
Thank you!!

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Symposium 2019 : Gestion de projet en Intelligence Artificielle

  • 2. About me 2 Master in Bioinformatics Strasbourg University (France) Ph.D. In Pharmaceutical Science. Strasbourg University (France) Post-Doc at McGill (Computational chemistry) Post-Doc at UdeM (Computational Biology) Senior Data Scientist at Mnubo (IoT company) Nathanael Weill
  • 3. What is AI? Why AI? AI project phases Warnings Optimize the process Outlines 3
  • 4. The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. (google dictionary) What is AI? 4 Prediction: The process of filling in missing information. Prediction takes data you have to generate data you don’t have.
  • 5. How does it work? 5 computer Input data Output Function computer Input data Function Output computer New Input data Prediction Function
  • 9. Big Data & Data Science Projects Failure Rate 9 GARTNER ESTIMATED 85% of big data projects fail (2017). The initial estimation was 60% (GARTNER 2016) THROUGH 2020 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization. (GARTNER 2018) THROUGH 2022 20% of analytic insights will deliver business outcomes. (GARTNER 2018) EXECUTIVE SURVEY 77% respondents say that “business adoption” of big data and AI initiatives continues to represent a challenge for their organizations (NEWVANTAGE PARTNERS 2019)
  • 10. A recipe for failure We must define the solution as an entire process. If prediction is the end of the solution, the entire solution might fail because: • The output does not correspond to the operational needs. • The operator will not use it due to complexification of the process. • No one is capable of managing the algorithms if something goes wrong. • … Data Algorithm Prediction
  • 11. Data Algorithm Prediction Judgment Action Feedback Critical! We have to make sure we produce the right information and in the right format to help the person in charge to take action Manager: Person in charge to take action. We need to make sure this person is involved early in the process Design of the solution
  • 12. Identification of the problem to solve Design the appropriate solution Proof of concept Productization Scale the process Reorganize the company 6 Phases 12
  • 14. At Mnubo we designed a 3-5 days workshop with clients to go from the problem identification to the mock up of the solution Performance problem? Scalability issue? How to Consume the predictions? Maintain the solution? What action(s) will be taken? … Ex: 1 prediction per machine? Every hour? 12 hours? Solving the right problem 14
  • 15. A journey as a Data Scientist 1/2 15 Data Scientist: Define the valuable business problem Translate the business problem into a KPI A Key Performance Indicator (KPI) is a measurable value that demonstrates how effectively a company is achieving key business objectives. Organizations use key performance indicators at multiple levels to evaluate their success at reaching targets. Client: « I loose a lot of money when the assembly lines stops ». « I would like to reduce the number of machine failures ». https://www.klipfolio.com/resources/kpi-examples
  • 16. A journey as a Data Scientist 2/2 16 Data Scientist: Define the metric and the definition of success. Next phase: Proof of concept. • explore • Establish a baseline • Iterate!!! Client: A success would be to predict failure 12 hours in advance with an accuracy of 80% According to the final report, I get an answer to: • Is the objectives reasonable? • How should I productize the solution?
  • 17. POC: Critical choice 17 Time Resources Accuracy • Explore • Create a baseline • Iterate Agile
  • 18. Productization phase 18 2 productization models: • Data scientist write specifications and engineers take over and rewrite the code in an other language (java, scala…) • Data scientist with a team of data engineer, dev ops etc… takes the code written and deploy it in the infrastructure Pros and cons…
  • 19. Data Algorithm Prediction Judgment Action Feedback Data Algorithm Prediction Judgment Action Feedback Data Algorithm Prediction Judgment Action Feedback Data Algorithm Prediction Judgment Action Feedback Data Algorithm Prediction Judgment Action Feedback Data Algorithm Prediction Judgment Action Feedback Data Algorithm Prediction Judgment Action Feedback Full solution management: • Configuration • Monitoring • ROI evaluation Scaling of the Solution Avoid silos labyrinthine system
  • 20. Data Algorithm Judgment Action Feedback Data Algorithm Prediction Judgment Action Feedback (Automating)
  • 21. Dev ops: In charge of deploying and maintaining the infrastructure to support the solution Data engineer: in charge of setting the appropriate resource to access the data. Data scientist: in charge of creating the machine learning model (pipeline data to prediction) Roles: development phases 21
  • 22. Operator: In charge of activating/deactivating the algorithms designed for specific predictions/actions => Provide feedback to data scientists Data scientist: Integrate the feedback and update the algorithm (if needed) Dev ops: Maintain the infrastructure Roles: long term 22
  • 24. 24 The Proof of Concept Curse in AI and IoT 80% of companies stop at the POC stage. Laggards & Winners
  • 25. I recommend: To use Agile methodology in all phases of the project Have a clear understanding of the final aim in term of: • The process of development • The perturbation of the company organization Critical role of the project manager 25 Phases: Identification of the problem to solve Design the appropriate solution Proof of concept Productization Scale the process Reorganize the company
  • 26. There is multiple tracks that can be done in parallel: Data acquisition To make sure the data are available in (near-) real time. Creation of the machine learning algorithm Create the appropriate pipeline to train, test and deploy the model(s). Creation of the end point to expose the predictions A dashboard, an app, an alerting system, a reporting system. Monitoring of the pipeline monitor the data acquisition, the performance of the model, the use of the end point… Process to capture the action taken and consolidate a feedback loop Optimize the process 26
  • 27. Hofstadter's law: It always takes longer than you expect, even when you take into account Hofstadter's Law. First AI project is hard, you should start with an easy project • Is there already a system in place to monitor the KPI? • Is the data pipeline already in place? • Is AI a replacement for an existing system? Assess the client maturity is hard especially regarding the company perturbation A good PM is the key to success! Wrap up 27