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Understanding Products Driven by Machine Learning and AI: A Data Scientist's Perspective

When describing a product as "data-driven" or "fueled by machine learning," it can be difficult to parse a common, fundamental definition of what makes an application "intelligent." In this talk, we will cover how you can peel back the buzzwords from the space of data science, machine learning, and artificial intelligence. You will be able to:
- Start sketching your own frameworks for understanding and evaluating these products
- Better understand how things can go wrong
- Know what questions to ask product vendors, and have a better understanding of their answers
- Learn more about data science as a process, as people organizations, as a product and as a service

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Understanding Products Driven by Machine Learning and AI: A Data Scientist's Perspective

  1. 1. @amcasari Understanding Products Driven by Machine Learning and AI: A Data Scientist’s Perspective A.M. Casari Principal Product Manager + Data Scientist Concur Labs @ SAP Concur
  2. 2. @amcasarihere to there via random walk senior data scientist @ SAP Concur control systems engineering + robotics + legos officer in USN operations research analyst wandering dirtbag + conservation volunteer EE + applied math + complex systems underwater robotics consultant extraordinaire SAHM product + data @ Concur Labs
  3. 3. @amcasari path forward today §definitions §data science as process §data science as people §data science as product §data science as service
  4. 4. @amcasari when we say… DATA SCIENCE § We mean….the interdisciplinary intersection of methods, processes, algorithms and problem solving techniques to extract knowledge from data1 MACHINE LEARNING [ML] § We mean….the statistical class of algorithms which allow us to systematically improve a computer’s ability to perform a given task2 DEEP LEARNING [DL] § We mean….the family of ML methods based on learning data representations3 ARTIFICIAL INTELLIGENCE [AI] § We mean….when a machine mimics cognitive functions usually observed in animals, such as problem solving and creativity4 hint:AI has not happened yet… + our community is well represented inWikipedia
  5. 5. @amcasari DL is ML is AI h/t @mlhassett AI DL ML
  6. 6. @amcasari when we say products are… DATA DRIVEN § We mean….product strategy and engineering decisions are made by qualitative + quantitative analysis of data INTELLIGENT § We mean….users interact with features that thoughtfully and seamlessly balance context and useful information FUELED BY MACHINE LEARNING § We mean….somewhere in the backend, someone is using data with some kind of predictor. More or less. AUGMENTED § We mean….intelligent products which guide users through a new experience without distracting from their purpose
  7. 7. @amcasari data science as … a process
  8. 8. @amcasari avoid this… xkcd #1838
  9. 9. @amcasari more like this…
  10. 10. @amcasari more like this… idea research exploration hypotheses model outcomes feedback
  11. 11. @amcasari when to move on? “Models are not right or wrong; they're always wrong. They're always approximations. The question you have to ask is whether a model tells you more information than you would have had otherwise. If it does, it's skillful.” - Gavin Schmidt’s excellent TED Talk
  12. 12. @amcasari be responsible technologists § Algorithmic Accountability Review § Responsibility § Explainability § Accuracy § Auditability § Fairness § Example Guiding Questions § How could this go south? § What social constructs am I modeling implicitly or explicitly? § What are the impacts of the choices I have made in my data modeling + feature selection? § Could the deployment of this work negatively impact a subset of my users?
  13. 13. @amcasari data science as … people explained through xkcd art
  14. 14. @amcasari data science as a team sport v0 Cross Functional Team Cross Functional Team Data Scientist
  15. 15. @amcasari data science as a team sport v1 Cross Functional Team Data Scientist Team Cross Functional Team Cross Functional Team
  16. 16. @amcasari data science as a team sport v2 needs - “define the primary stages of leveraging Big Data with stakeholders representing the domain. analysts usually drive from discovery toward integration, while the engineers tend to drive from systems toward integration NB: effective, hands-on management in Data Science must live in the space of integration, not delegate it” roles - “leverage different disciplines, opportunities, and risks... there’s great power in pairing people with complementary skills, in team environments where they can recognize each other’s priorities and perspectives blurring these roles is wonderful... however, when businesses get into trouble, they also tend to “push down” these roles, blurring boundaries in ways which stresses teams and limits scalability” diagram and description courtesy of Paco Nathan
  17. 17. @amcasari data science as a team sport vNOW Advanced Engineering Team Data Science Team Cross Functional Team Cross Functional Team Research Team Applied Research Team
  18. 18. @amcasari Data science as … a product
  19. 19. @amcasari data products v1 @MROGATI
  20. 20. @amcasari data products vNEXT diagrams and description courtesy of Paco Nathan The playbook on this is being written now… personal digital stylists via StitchFix augmented writing via Textio artwork generation via Netflix games to teach computers via Google
  21. 21. @amcasari be responsible companies § Design for Fairness § Design for Accountability § Design for Transparency § Design for Privacy § Design for Ethics § Example Guiding Questions § Who is responsible if users are harmed by this product? § Who will have the power to decide on necessary changes to the algorithmic system during design stage, pre-launch, and post-launch? § How much of your system / algorithm can you explain to your users and stakeholders? § What are realistic worst case scenarios in terms of how errors might impact society, individuals, and stakeholders?
  22. 22. @amcasari data science as … a service
  23. 23. @amcasari comparing services + vendors § Why are you asking for my data? § Cold-start versus warm-start § Evaluation comparison § How can your models work for me? § Feature Transfer § Define results by your business value, not their metrics § All your services should uphold your data science standards: Fairness, Accountability, Transparency, Privacy, Ethics § What questions should I be asking about their processes? § Privacy > GDPR compliant? § Where does the data live § Where do your services live § Who owns the trained models once they are trained
  24. 24. @amcasari evaluating during buy or build § Do you have an defensible moat around this data? § How long of a project runway would you have to build a team? § Do you have internal resources who you could leverage + build out a new team? § As this project/product scales, will the cost of the services keep up with your ARR? § What future-thinking, vertical specific brainshare are you paying someone else to gain?
  25. 25. @amcasari Choose Your Own Educational Adventure Data science / ML / AI needs everyone Approachable Resource Recommendations Books! • Python for Data Analysis, William McKinney • Doing Data Science, Cathy O’Neil + Rachel Schutt • Data Science from Scratch, Joel Grus • Machine Learning with Python Cookbook, Chris Albon MOOCs! • Machine Learning, by Andrew Ng on Coursera • Machine Learning Specialization, by Emily Fox + Carlos Guestrin on Coursera • fast.ai, by Jeremy Howard + Rachel Thomas
  26. 26. @amcasari thank you

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