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• Evaluation: It is the testing phase of the AI project cycle, where we check if the model can achieve required
goals or not. If the model is not fulfilling the requirements, the model or even the data can be changed. Once
the developer feels the project is ready, the project will be put into working conditions and then deployed and
handed over to the user. If the deployment stage is not reached, the project is of no use.
• Deployment: In this stage, we integrate the best-performing model into the production environment, setting
up continuous monitoring, and maintenance to sustain performance over time.
Problem
Scoping
Data
Deployment Acquisition
Data
Evaluation Exploration
Modelling
Why We Need an AI Project Cycle?
The AI project cycle is a structured framework comprising stages from problem definition and data acquisition to
model development and deployment. It involves identifying objectives, gathering data, exploring and modelling
data, evaluating outcomes, and deploying AI solutions. This iterative process ensures systematic development,
validation, and improvement of AI applications aligned with business goals and user needs. We use an AI project
cycle for these important reasons:
• Structure and Organisation: Provides a clear roadmap and systematic approach for planning, executing, and
managing AI projects, ensuring all steps are followed in a logical sequence.
• Efficiency: Optimises resource allocation, time management, and task prioritisation throughout the project
lifecycle, leading to more effective and timely project outcomes.
• Risk Management: Facilitates early identification and mitigation of risks related to data quality, model
performance, deployment challenges, and ethical considerations, minimising potential disruptions.
• Quality Assurance: Ensures rigorous testing, evaluation, and validation of AI models to meet desired performance
standards and business requirements, enhancing reliability and usability.
• Continuous Improvement: Supports iterative development and enhancement of AI solutions based on
feedback, new data insights, and evolving business needs, fostering innovations, and adaptation over time.
• Modularity: Encourages the design of AI solutions in a modular fashion, allowing components to be developed,
tested, and integrated independently, promoting flexibility and scalability in project development.
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