A collection of comprehensive, up-to-date, and high-quality educational resources about building AI systems.

Why Read This Guide?

You’ve been amazed by recent tech demos of AI systems, and have a great use-case for AI but are not sure where to start.

You’re building an AI system, but its performance is sub-par and you want to efficiently improve it.

You’re an engineer of AI-powered software, and want to deploy it efficiently and reliably in production.

Does this sound like you? Read on.

The Inspired AI Guide is aimed to provide a birds-eye view of the whole process of developing a user-facing AI system, from conceptualization to deployment. The content is curated by Inspired Cognition’s team of AI experts to ensure that it is comprehensive, up-to-date, and reliable. We also encourage you to get in touch with us if you would like to ask any questions, make any content suggestions, or contribute to the AI Guide.

Steps in Building a User-facing AI System

Building a functional AI system requires a number of steps. The following tutorial covers the essentials of going from an idea to a working AI system that you can actually use. Click through the sections for details.

Should I Use AI for my Problem? Should I Use AI for my Problem?
Identify the value provided by solving the problem, with AI or not. Verify that an AI-based solution is both necessary and feasible.
Formalizing your AI Problem Formalizing your AI Problem
Define your problem in a way that can be solved by an AI-based system, choosing your desired inputs and outputs.
Deciding an Evaluation Method Deciding an Evaluation Method
Decide a method to rigorously evaluate system performance and validate the initial solution that you will be creating.
AI Data Creation AI Data Creation
Create data that you can use to test, and possibly train your AI system.
Choosing or Building an AI Solution Choosing or Building an AI Solution
Choose or create a model that can provide an initial solution to your problem.
AI Model Deployment AI Model Deployment
Deploy the AI-based solution to be available to users on the cloud, local servers, or edge devices.
AI Monitoring and Debugging AI Monitoring and Debugging
Monitor your system to make sure it is working as expected, and find failure cases where it may be underperforming.
Iterative System Improvement Iterative System Improvement
Iteratively improve the system performance by modifying your data or methods.

Sections by Role

Depending on your role in an AI-driven project you will likely be involved in different steps of the system building process.

  1. Executives or Team Leaders: You are the head of an organization deploying AI-based systems. You may not want to get into the detail of exactly how systems are implemented, but you will certainly want to know whether they are delivering value in your organization’s workflow.
  2. Data Scientist or Research Scientist: Your job is to create the best AI-based prediction methods possible. Your core skills are implementation of machine learning models, analysis of results, and iteration.
  3. Software Engineer: You are in charge of fitting AI within a larger software system. You will be performing deployment, versioning, and integration with user-facing interfaces.
  Executive/ Leader Data/Research Scientist Software Engineer
Should I Use AI? Yes    
Formalization Maybe Yes  
Evaluation Maybe Yes Maybe
Data Creation   Yes Maybe
Choosing/Building a Solution Maybe Yes Maybe
Deployment     Yes
Monitoring/Debugging Maybe Yes Yes
Iterative Improvement   Yes Maybe