AI Product Development: Technical Feasibility and Prototyping

Course details

In this course, learn how to evaluate the technical feasibility of AI solutions by considering critical factors such as data availability, resources, ethical implications, technology constraints, and security concerns. Instructor Maria Parysz shows you how to select appropriate AI models and tools that align with specific project requirements, ensuring optimal performance and compatibility. The course provides hands-on experience in building AI product prototypes, enabling you to transform concepts into tangible solutions for initial testing and demonstration. Master techniques to evaluate prototype performance rigorously, using metrics and analysis to identify areas for improvement. By incorporating feedback and refining prototypes, you can ensure that your AI solutions are both effective and aligned with user and business needs. This comprehensive approach empowers you to move from ideation to implementation confidently, with a strong foundation in AI feasibility and prototyping.

Learning objectives
Evaluate if AI solutions are technically implementable based on several factors like data, resources, ethics, technology, and security.
Understand how to choose suitable AI models and tools tailored to specific project needs.
Develop AI product prototypes to test and demonstrate initial concepts.
Refine AI prototypes by evaluating performance and incorporating feedback.

Concepts

0. Introduction

01 - Introduction

1. What Is Feasible

02 - The goal - Feasibility and prototyping
03 - Building options

2. Tech Feasibility in Detail

04 - Must knows for feasibility
05 - Proof of concept, part 1
06 - Proof of concept, part 2
07 - Core AI architecture concepts, part 1
08 - Core AI architecture concepts, part 2
09 - How to do tech feasibility
10 - Questions for different implementation options
11 - Who can help you out Storage and computing power
12 - Architecture, latency, standalone vs. connected
13 - Security, ethics, and compliance
14 - Endpoints and data
15 - Talent
16 - Maintenance and retraining
17 - Scaling and testing
18 - Metrics and time and budget updates
19 - Best practices of working with vendors

3. Prototyping

20 - Fundamentals of prototyping and prototyping timeline
21 - Prototyping roles, personas and expected outcome
22 - Minimum viable product (MVP)
23 - Six strategies for building prototypes, part 1
24 - Six strategies for building prototypes, part 2
25 - Prototyping best practices
26 - The process of gathering feedback from users
27 - Best practices for gathering feedback from users
28 - Drawing conclusions after the feedback
29 - Demo

Conclusion

30 - Next steps

AI Product Development: Technical Feasibility and Prototyping

Authors

Maria Parysz