Slides

Lessons learnt from hundreds of ML Projects

Feb25_2022_Lesson_learnt_from_hundreds_of_ML_projects.pptx

Visualization for Data Science

Visualization-DesignThinking

Visualization for Data Science

Focus on 1) User. 2) Task or Decision user need to make 3) Data and information

SaaS product

MVP to Product

  • Identify use case

  • Build demoable solution

  • Add automated deployment

  • Capabilities to extend to many customer

  • Increase utility

Customer Feedback

Kano Model

Kano Model

  • Use list of features

  • Use 2 dimensions - functional utility, customer satisfication

  • Determine which features are satisfying or exceeding customer need

Build vs Buy Flow chart

Whether to build AI or buy AI tools

Build vs Buy

  • Build strategic capability

  • Buy routine

For data driven capability above becomes more complicated. See slide

How to lead and structure ML Projects

GCP Online Meetup #49: Managing ML Projects

How to Manage ML Projects

  • Convert use case to ML Use case

  • Determine elephant and monkey tasks

  • Determine whether more data, different data, different algorithm will produce results


Test

Experiment prep

Field Research Experiment Steps

Before

  • Identify Research Question, Justify Why experiment is needed, Define Null Hypothesis, Do Experiment design

During

  • Randomization, EDA, Modeling, Analysis, Conclusion

MVP - Minimum Viable Product

MVP Canvas

MVP Canvas


GTM Slides

GTM Slides

GTM Strategy slides

  • Strategy MAP, GTM Roadmap, GTM Planning

  • Product Market Fit, Customer Value MAP, Brand positioning

  • Unique selling proposition, Product value proposition