AI Project Cycle

 

Artificial-Intelligence Project Cycle :--- 

 

Building your own AI-model that predicts future outputs is amazing, here I'm gonna talk to you about what is the process or you can say methods for creating a well defined AI model.

1. Problem

In the very first process you have to look out for 4Ws principal. Let's understand what is 4Ws principal, it is who?, why?, what?, where?. You have to answer these questions like, what is the problem, why you want to solve it, who will get benefited with it, where is the problem. If you are able to identify these answer in your model then do follow the second step.

2. Data Acquisition

This is the second process of AI-cycle in which you have to find the relevant information related to your problem or you can say topic. you can find it on ---> data.world , data.gov.in and many more.

3. Data Exploration 

In the third process you have to look for whether your data is correct or not. You can find it by checking null values, number of values in a particular column, their data types etc...

4. Modelling 

In fourth process of AI-model you have to replicates a decision process to enable automation and understanding. AI/ML models are algorithms that are “trained” using data and human expert input to replicate a decision an expert would make when provided that same information.

5. Evaluation 

Evaluation is the fifth process of an AI-model in which you are supposed to make an AI/Ml to learn from the given data through Neural Network. This part is considered to predict the more accurate output.

6. Deployment

This is the last process of an AI- model, once your model is gone through testings, the AI-operation team deploys the model into production. According to IBM, Deployment is the process of configuring an analytic asset for integration with other applications or access by business users to serve production workload at scale. 

 

AI-model on Detect Possibility of being covd-19 Positive and negative  

 

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