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• Deployment
✶ Deploy model: Integrate the selected model into the company's customer management system to predict
churn risk for new customers.
✶ Monitor performance: Monitor the model's predictions in real-time, track churn rates, and gather feedback
from customer service interactions.
In this example, each phase of the AI project cycle builds upon the outputs of the previous phase:
AI Project Cycle Mapping Template
Data Data
Problem Scoping Modelling Evaluation Deployment
Acquisition Exploration
The Gather customer Analyse Select machine Evaluate Integrate the
telecommunications demographics, customer learning each model's model to predict
company wants to usage patterns, demographics, algorithms for performance new customer
reduce customer service history, usage patterns, classification, using accuracy, churn risk.
churn rates. and churn and churn like logistic precision, recall,
status data rates with regression, and F1-score.
from company visualisations decision trees,
databases. and statistical and random
summaries. forests.
• Problem Scoping: States the problem that needs attention.
• Data Acquisition: Data acquisition consists of two words: Data and Acquisition. Data refers to the raw facts,
figures, information, or statistics; where as, acquisition refers to acquiring data for the project.
• Data Exploration: It is the first step of data analysis that is used to visualise data. It generates insights that are
used to inform modelling decisions.
• Modelling: Develops predictive models based on insights gained from data exploration.
• Evaluation: Assesses model performance by feeding the data into the model and comparing the output with
the actual answers. It is used to determine the best model for deployment.
• Deployment: Integrates the selected model into the company's systems for real-world usage.
The feedback loop continues as the deployed model's performance is monitored, and insights gathered are used
to refine future iterations of the AI solution.
AI Ethics
Ethics are rules about what is right and wrong. AI ethics are rules about using artificial intelligence (AI) in a good
way. As the use of AI increasing, companies are creating rules called AI codes of ethics.
An AI code of ethics is a set of rules that says how AI should be used to help people. These rules allow people make
good decisions when using AI.
Some of the AI ethics that you need to follow while working on an AI model are:
• Model should be understandable by all.
• Every aspect of the model should be self-explanatory and transparent.
• Covering all the sections of the population is important.
138 Artificial Intelligence Play (Ver 1.0)-IX

