ProductizeML
  • ProductizeML
  • Introduction
    • Objectives
    • About the Course
    • Guidelines
    • Syllabus
    • After Completion
  • Machine Learning
    • Why ML, and why now
    • Supervised Learning
    • Unsupervised Learning
    • Deep Learning
    • ML Terminology
  • Data Management
    • Data Access
    • Data Collection
    • Data Curation
  • Train and Evaluate
    • Framework and Hardware
    • Training Neural Networks
    • Model Evaluation
  • Productize It
    • ML Life Cycle
    • Business Objectives
    • Data Preparation
    • Model Development
    • Train, Evaluate, and Deploy
    • A/B Testing
    • KPI Evaluation
    • PM Terminology
  • Resources
    • Readings
    • Courses
    • Videos
  • Hands-On
    • Python for Machine Learning
      • Python Installation
        • MacOS
        • Linux
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  1. Productize It

PM Terminology

You will learn: commonly used product management terminology.

Glossary

  • MVP (Minimum Viable Product): the first launched version of your product to customers. It is developed with a minimum number of features to solve your problem in a simple way. It is generally used as a trial tool only.

  • MLP (Minimum Lovable Product): similar to an MVP, but with special consideration for its design and UI. It aims to solve the problem, but also delight users.

  • MMP (Minimum Marketable Product): this is the version of your MVP (or MLP) which you will launch to market.

  • Business Impact Analysis (BIA): predicts the consequences of disruptions to a business by collecting data and measuring metrics to develop alternative strategies if our proposed solution fails.

  • KPI (Key Performance Indicator): metric to measure and evaluate the success of an organization or certain activity towards achieving a goal.

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Last updated 2 years ago

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