OpenTalks.AI - Эмели Драль, PA и RS - обзор текущего применения в бизнесе​

Ad8ae7af280edaecb09bd73a551b5e5f?s=47 OpenTalks.AI
February 21, 2020

OpenTalks.AI - Эмели Драль, PA и RS - обзор текущего применения в бизнесе​



February 21, 2020


  1. 2.

    About me • Co-founder Mechanica AI • Ex Chief Data

    Scientist at Yandex Data Factory • Co-founder of Data Mining in Action, largest offline data science course in Russia • Co-author of two Coursera specializations in data science with > 50K students • Lecturer at Harbour.Space University, GSOM MBA ?! 50+ Industrial applications of machine learning
  2. 4.

    Technologies Future trends Interpretable predictions for robust decision support Optimization

    of key business KPIs, full process automation Question What will happen in the future? Which action to take? Parametric models Machine learning Physical models Statistical models Expert systems …. Predictions VS recommendations
  3. 6.

    Trend 1: from standard use cases EXISTING BUSINESS PROCESS BETTER

    PREDICTION QUALITY + Image source:
  4. 9.

    Trend 2: to explainable models root cause analysis ≠ explainable

    model How the model works? How the model made this specific prediction? Which factors are used?
  5. 10.

    Trend 2: explainable models Predict which steel coils are likely

    to have a defect of each specific group For highly suspicious coils – indicate top factors in prediction
  6. 13.

    Trend 3: scenario analysis Predict which steel coils are likely

    to have a defect of each specific group …for each possible production route the engineer has available Route 1: 42% Route 2: 60% …
  7. 14.

    Summary: trends in predictive analysis New use cases + Explainability

    Scenario analysis + Making predictive models usable by business and domain experts
  8. 17.

    Trend 1: to process/decision optimization Traditional decision support (parametric, rule-based

    models, technological maps…) Now with machine learning! The “Sniper” service was introduced as a pilot program in the production process, optimizing the consumption of ferroalloys and additional materials in steelmaking.
  9. 18.

    Trend 2: hybrid models machine learning + human in the

    loop machine learning + first-principle models Content moderation Industrial production optimization
  10. 21.

    Key trends 1. From standard to novel use cases and

    product features based on ML 2. From black box to explainable AI 3. Scenario analysis with ML 4. From item/content recommendations to process/decision optimization 5. Hybrid applications based on combination of ML with other tools 6. From recommendations to automation of action