Announcing the Inspired AI Guide

Announcing the Inspired AI Guide

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Today, there is so much information about AI online, and even if you are interested in building an AI-driven app or product it is often difficult to know where to start. To solve this problem we have created the Inspired AI Guide, which provides 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.

For example, it is good for readers who:

  • are amazed by recent tech demos of AI systems, and have a great use-case for AI but are not sure where to start.
  • are building an AI system, but its performance is sub-par and you want to efficiently improve it.
  • are an engineer of AI-powered software, and want to deploy it efficiently and reliably in production.

It covers topics such as:

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.

If this sounds interesting to you, please check out the AI Guide, and get in contact with any questions, comments, or content suggestions!