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Symposium 2019 : Gestion de projet en Intelligence Artificielle

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L’objectif d’un projet impliquant l’intelligence artificielle est d’accélérer la prise de décision, voir même, d’automatiser les actions qui doivent être effectuées dans le cadre d’une tache. La principale difficulté est qu’il n’est pas possible de savoir à l’avance quelle méthode d’AI permettra d’atteindre l’objectif. La gestion du projet est souvent atypique et nécessite d’être flexible en respectant toutefois des contraintes de budget. Pour cette raison une approche waterfall est à éviter. Toutefois, nous allons voir qu’elle peut être exploitée dans certaines phases du projet.
Lors de cette présentation, nous allons voir les trois phases du projet : prototypage de la solution, mise en production, ainsi que les stratégies de maintien à plus long terme de la solution.

Dr. Nathanael Weill

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Symposium 2019 : Gestion de projet en Intelligence Artificielle

  1. 1. Project Management in AI Nathanael Weill, PhD
  2. 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. 3. What is AI? Why AI? AI project phases Warnings Optimize the process Outlines 3
  4. 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. 5. How does it work? 5 computer Input data Output Function computer Input data Function Output computer New Input data Prediction Function
  6. 6. Why AI? 6 Prediction became cheaper
  7. 7. Data AccuracyClient The AI race 7
  8. 8. 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)
  9. 9. 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
  10. 10. 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
  11. 11. Identification of the problem to solve Design the appropriate solution Proof of concept Productization Scale the process Reorganize the company 6 Phases 12
  12. 12. Use Case 13
  13. 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
  14. 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
  15. 15. 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?
  16. 16. POC: Critical choice 17 Time Resources Accuracy • Explore • Create a baseline • Iterate Agile
  17. 17. 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…
  18. 18. 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
  19. 19. Data Algorithm Judgment Action Feedback Data Algorithm Prediction Judgment Action Feedback (Automating)
  20. 20. 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
  21. 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
  22. 22. Company perturbation 23 IT Team Operation Team Executives Data Science Team
  23. 23. 24 The Proof of Concept Curse in AI and IoT 80% of companies stop at the POC stage. Laggards & Winners
  24. 24. 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
  25. 25. 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
  26. 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
  27. 27. Nathanael Weill nweill@mnubo.com 28 Thank you!!