What is Artificial Intelligence and its Lifecycle

The simulation of human intelligence, which helps to processes by machines, particularly computer systems, is known as artificial intelligence. Expert systems, natural language processing, speech recognition, and machine vision are examples of AI applications. To learn more about What is Artificial Intelligence and its Lifecycle, Join the Artificial Intelligence Course in Chennai at FITA Academy.

Artificial Intelligence Lifecycle:

The Artificial Intelligence Lifecycle can be classified accordingly with various steps. Artificial Intelligence Lifecycle is classified into various sections:


Understand the Problem:

You must first establish the major project objectives and requirements to communicate your team’s grasp of their mission difficulty. Then, from a business standpoint, specify the desired outcome. Finally, AI will be able to tackle this problem. Learn more about this phase in the section on framing AI problems.

Data Gathering and Exploration:

This step concerns gathering and analysing the data needed to construct the AI solution. This includes locating existing data sets, detecting data quality issues, and developing preliminary insights into the data and thoughts on a data strategy.

Data Wrangling and Preparation:

This phase includes all operations required to create the working data set, which is the initial raw data that the model can use in an ordered manner. This stage might be time-consuming and difficult, but it is vital for developing a model that meets the objectives specified in Step 1.



This process involves experimenting with data to find the best model. During this phase, the team frequently trains, tests, assesses, and retrains various models to discover the optimum model and settings to accomplish the intended goal.

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Once one or more models that appear to perform well based on relevant assessment criteria have been developed, test the models on additional data to confirm they generalise well and achieve the business objectives.


Move to Production:

Deploy a model into a production environment once designed to fulfil the desired outcome and performs at a level declared ready for use on live data. In this situation, the model will incorporate fresh data not included in the training session.

Monitor Model Output:

Monitor the model’s performance while it processes this live data to confirm that it can produce the desired result—a process known as a generalisation or the model’s capacity to adapt to new, previously unknown data. Models in production can “drift,” meaning their performance will alter over time. Drift must be carefully monitored, and the model may need to be updated regularly.


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By Mayuri