Machine Learning Sample Feature Spec

Transcript

1. Sample Feature Spec Question Comments/Hints Answer Define the problem Please provide a brief description of the problem and why is it important to solve this problem What strategic outcome can it drive ? Choose one of the three and elaborate - Enhanced experience and functionality for customers - Improvements in internal functions, processes and business logic - Expansion to new vericals (Refer Appendix A) Which category of machine learning does it fall under and why ? - Mass customization - Automate Tasks - Automatic retrieval or processing of content - Predictions - Detecting anomalies (Refer Appendix B) ML for Enterprise PMs divs.raghavan@gmail.com
2. Go ahead and briefly describe a potential solution to the problem Think how you would describe this to a layman.Feel free to draw a mock if it makes it easy. ML for Enterprise PMs divs.raghavan@gmail.com
3. What kind of data do you need to store to make this feature work ? Think of all potential inputs found internally or externally Can you collect the data frequently ? Or is it a one time data ? Optional What’s your expected output ? ML for Enterprise PMs divs.raghavan@gmail.com
4. Does precision matter in this scenario ? Does explainability and interpretability matter ? Do you need to account for scenarios where you need to present some underlying data to make the results believable ? Whats the non ML alternative to this feature ? What happens when the model fails ? What’s your fallback scenario so that you don't lose user’s trust ? ML for Enterprise PMs divs.raghavan@gmail.com
5. Appendix A • Enhanced experience and functionality for your customers ◦ Do I have clear understanding of my customer segments ? ◦ How can I identify good customer experience from bad customer experience ? ◦ How can I personalize my end user’s experience with the product ? ◦ What are the decisions and choices I’m asking my customer to make today ? Can those choices be automated ? • Improvements in internal functions, processes and business logic ◦ What type of data do people in my own company work with ? Can we automate gathering and working with this data ? • Expansion to new verticals and new products ◦ Can we create brand new products for existing customers ? ◦ Who else can benefit from our data and insights ? ML for Enterprise PMs divs.raghavan@gmail.com
6. Appendix B Mass customization of the system and user experience - Optimized price - Personalization - Recommendation Automate Tasks - Data entry into CRM system. - Filing expense reports Automatic retrieval, generation or processing of content - Ranking and relevance of search results. - Finding relevant conversations Predictions, estimates and trends at scale - Sales Forecasting - Identifying trends in renewal threats - Problems in sales pipelines - Customer Segmentation Detection of unusual activity or system failure - 1:1 reporting on unusual system usage - Resource utilization issues in cloud environment ML for Enterprise PMs divs.raghavan@gmail.com