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AI Product Foundations: Planning Strategies for Data Scientists

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The rise of generative AI has elevated product management from a niche competency to an essential discipline for data scientists. Those who can wield technical expertise with strategic execution will shape the future of AI-driven industries. In this course, Matthew Blasa distills the principles of AI product planning, offering structured frameworks and pragmatic strategies for those operating at the intersection of data science, product management, and leadership.

Learn to define AI products with precision, construct a mental framework for execution, and assess your career trajectory with clarity. The course also navigates the complexities of data governance, quality, and project delivery—elements that determine whether AI initiatives succeed or collapse under their own weight. By the end of this course, you’ll be equipped with the communication skills you need to command a room, translate data into strategic insight, and align AI initiatives with business imperatives.


01 - Introduction
  • 01 - AI Product the hidden opportunity

  • 02 - 1. Why AI Products Matter
  • 01 - Why do data scientists need an AI product
  • 02 - Essential skills for AI product development
  • 03 - Key players in AI product creation
  • 04 - Taking an AI product mindset

  • 03 - 2. Defining the AI Product Landscape
  • 01 - Understanding the product market
  • 02 - Components of AI products
  • 03 - The AI product lifecycle
  • 04 - Exploring types of AI products

  • 04 - 3. Identifying AI Opportunities
  • 01 - Opportunity discovery
  • 02 - Defining the value of an AI product
  • 03 - Start with users
  • 04 - Building support

  • 05 - 4. Technical Foundations Data and Algorithms
  • 01 - Data is the foundation
  • 02 - Data maturity
  • 03 - Data curation
  • 04 - Organizing AI requirements
  • 05 - AI models for product success

  • 06 - 5. Platforms and User Experience
  • 01 - The role of platforms in AI products
  • 02 - Solving the last mile problem
  • 03 - User journeys
  • 04 - Human in the loop

  • 07 - 6. Maximizing Value AI Productization
  • 01 - Defining an MVP
  • 02 - Product vs. novelty Creating valuable AI solutions
  • 03 - Personas
  • 04 - Experimentation
  • 05 - Monetization and pricing strategies
  • 06 - Finding and measuring ROI
  • 07 - Strategies for going to market

  • 08 - 7. Sustaining AI Product Success
  • 01 - Why data strategy is core to AI strategy
  • 02 - Updating AI models for continued success
  • 03 - Optimizing development workflows
  • 04 - Evaluating and tracking AI product performance

  • 09 - Conclusion
  • 01 - Continuing your AI product learning journey
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    شناسه: 45472
    حجم: 389 مگابایت
    مدت زمان: 105 دقیقه
    تاریخ انتشار: ۱۳ دی ۱۴۰۴
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    139,000 تومان
    افزودن به سبد خرید