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visual formats such as charts, graphs, and plots, data scientists can more easily interpret and draw insights from
the information. This analytical approach not only helps to reveal underlying structures within the dataset but also
facilitates the identification of anomalies or irregularities, ultimately laying the groundwork for more informed and
effective decision-making in subsequent stages of the AI project.
Stage 4 Modelling
The design phase is an important stage in the AI project cycle, ensuring that AI systems function effectively.
Data modeling is a foundational step within the AI development process, happening before model training. It
involves identifying and defining the relationships between different data entities, ensuring that the AI model can
understand the connections and dependencies within the data. The quality of the data model significantly impacts
the performance of the AI model, as well-structured data enables better learning and more accurate predictions.
Additionally, comparative analysis is performed to understand the advantages and disadvantages of each model. This
helps in identifying the most effective solution that aligns with the project goals and constraints. The design phase
often involves iterative refinement, where initial models are fine-tuned based on feedback or preliminary testing.
Stage 5 Evaluation
The testing phase of the AI project cycle is a critical step where the model's performance is evaluated to ensure it
meets the predefined goals and requirements. If the model does not fulfil the required objectives, modifications
may be necessary. Once the developer ensures the model achieves satisfactory results and aligns with the project's
goals, the AI project proceeds to the deployment phase. This means the project will be transitioned into an
operational state, where it is fully implemented and handed over to the end-user for practical use.
Stage 6 Deployment
In this phase, the best-performing model is seamlessly integrated into the production environment. This integration
involves implementing the model in a real-world setting where it can be used for practical applications. Additionally,
this stage requires setting up a system for continuous monitoring to ensure that the model consistently delivers
accurate and reliable results over time. Ongoing maintenance is also established to address any potential issues
that might arise, such as changes in the data, evolving requirements, or performance degradation. The goal is
to sustain the model's effectiveness, adapting as needed to maintain optimal performance and ensure that the
solution remains valuable and efficient throughout its lifecycle.
Reboot
1. Name and explain the 4Ws in problem scoping stage of AI project cycle.
2. Which visual tools will you use to transform the raw data into a visual format?
Consider a given scenario to understand the AI project cycle
A High School aims to enhance its waste management practices by encouraging students and staff to recycle
correctly. However, many people accidentally put waste into the wrong bins, which contaminates recyclable
materials and increases waste sent to landfills. To address this challenge, the school decides to develop an
AI-powered system capable of automatically identifying and sorting waste, thereby improving recycling efficiency
and reducing environmental impact.
The different stages of the AI project cycle for the given problem are as follows:
Stage 1 Problem Scoping
The goal of this stage is to define the problem and outline its objectives.
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