What is AI Project Cycle? What is the need of an AI Project Cycle?

What is AI Project Cycle?

The steps involved in AI project cycle are as given:


1. Problem Scoping :

The first step is Scope the Problem by which, you set the goal for your AI project by stating the problem which you wish to solve with it. Under problem scoping, we look at various parameters which affect the problem we wish to solve so that the picture becomes clearer


2. Data Acquisition :

Next step is to acquire data which will become the base of your project as it will help you in understanding what the parameters that are related to problem scoping.


3. Data Exploration :

Next, you go for data acquisition by collecting data from various reliable and authentic sources. Since the data you collect would be in large quantities, you can try to give it a visual image of different types of representations like graphs, databases, flow charts, maps, etc. This makes it easier for you to interpret the patterns in which your acquired data follows.


4. Modelling :

After exploring the patterns, you can decide upon the type of model you would build to achieve the goal. For this, you can research online and select various models which give a suitable output.

You can test the selected models and figure out which is the most efficient one.

The most efficient model is now the base of your AI project and you can develop your algorithm around it.


5. Evaluation :

Once the modelling is complete, you now need to test your model on some newly fetched data. The results will help you in evaluating your model and hence improving it.

Finally, after evaluation, the project cycle is now complete and what you get is your AI project.


The AI project cycle is an iterative process, and it may be necessary to revisit earlier phases if the results of the evaluation phase are not satisfactory. It is important to iteratively refine the model until it meets the desired performance criteria, and to continuously monitor the system’s performance to ensure that it continues to perform well over time.

In conclusion, the AI project cycle is a critical process for developing and deploying successful AI systems. By following this process, organizations can ensure that their AI systems are accurate, robust, and perform well in real-world scenarios.



What is the need of an AI Project Cycle?

Project cycle is the process of planning, organizing, coordinating, and finally developing a project effectively throughout its phases, from planning through execution then completion and review to achieve pre-defined objectives.

Our mind makes up plans for every task which we have to accomplish which is why things become clearer in our mind. Similarly, if we have to develop an AI project, the AI Project Cycle provides us with an appropriate framework which can lead us towards the goal.

The major role of AI Project Cycle is to distribute the development of AI project in various stages so that the development becomes easier, clearly understandable and the steps / stages should become more specific to efficiently get the best possible output. It mainly has 5 ordered stages which distribute the entire development in specific and clear steps:

These are Problem Scoping, Data Acquisition, Data Exploration, Modelling and Evaluation.

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