What is the AI project cycle and what are its important stages?

Artificial Intelligence is gaining rapid momentum and why wouldn’t it? It’s a big part of our future, in fact, it is our FUTURE! This field is so vast that you have to constantly keep learning about things related to Artificial Intelligence and beyond. So get ready to learn about the AI project cycle today.

What is Artificial Intelligence?

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Distinct applications of Artificial Intelligence include but are not limited to expert systems, natural language processing, speech recognition, and machine vision.


What is the AI Project Cycle?

The artificial Intelligence project cycle is the life cycle of an Artificial Intelligence project defining each and every step that an organization should follow to derive the business value from Artificial Intelligence to harness more ROI.

Understanding the Artificial Intelligence project life cycle helps in the identification of details that need to be attended to, such as more information about the deliverables and the job roles that need to be assigned at each stage. This helps you to gain more effective control of the project, and make it a success.

Generally, every Artificial Intelligence or data project lifecycle encompasses three main stages: project scoping, design or build phase, and deployment in production.

In the entire Artificial Intelligence project life cycle process, to solve a problem, we create a machine learning system called “model”, and this model is created by providing “training”. But to train a model, we need data, hence, the life cycle starts by collecting data. Let’s further find out what the stages of an Artificial Intelligence project life cycle are and why they’re important.


Stages of an Artificial Intelligence Project Life Cycle

Generally, there are 3 main stages to an Artificial Intelligence project life cycle. They are as follows:

Stage 1: Project Planning and Data Collection

This is the initial stage, which is very important and crucial. This stage explores the reasons why the project head decides to implement artificial intelligence solutions in their operations. It also anticipates whether or not the solution is tangible and profitable.

Stage 2: Design and training of the Machine Learning (ML) model

To choose the right Machine Learning model, a number of factors need to be considered, including the challenges faced by the business, desired results to be achieved, and the quantum and composition of data amongst other things. Some types of ML Models that are used include the Binary Classification model, multiclass classification model, and regression model.

Stage 3: Deployment and maintenance

So in this last stage, an environment with a web interface or an application where the new data can flow, giving the ML models a chance to show their analysis in the new interface shall be implemented. Merely launching the project doesn’t mean that it’s completed. It is extremely important to monitor, review and ensure that the solution helps produce the desired output.

Now that you know what an Artificial Intelligence project life cycle is and what are its different stages. Some of these stages might be more difficult, complex, and intricate than others. Knowledge about artificial Intelligence is a great asset that we all should possess. Don’t forget to let us know once you’ve started your journey in the world of AI with a quick comment down below! After all, we also want to know how you managed your project life cycle with AI.

If you haven’t already, don’t forget to check out Kaggle Experts: What is this new fad in town?

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