CBSE Class 9 AI (Artificial Intelligence) | 2. AI PROJECT CYCLE by Abraham | Learn Smarter
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Knowledge
2. AI PROJECT CYCLE

The AI Project Cycle is a structured process essential for developing effective AI systems, encompassing five stages: Problem Scoping, Data Acquisition, Data Exploration, Modelling, and Evaluation. Each stage is critical for ensuring the resultant AI model is accurate, reliable, and ethical. Careful attention to each step helps prevent biased results and maximizes the impact of AI projects.

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Sections

  • 2

    Ai Project Cycle

    The AI Project Cycle consists of five essential stages that guide the development of AI systems from identifying problems to deployment and evaluation.

    Learning Practice

  • 2.1

    Problem Scoping

    Problem scoping involves understanding and defining the problem that the AI system aims to solve.

    Learning Practice

  • 2.2

    Data Acquisition

    Data Acquisition involves collecting the necessary data for an AI project.

    Learning Practice

  • 2.3

    Data Exploration

    Data Exploration involves analyzing collected data to identify patterns, clean errors, and prepare the dataset for AI model training.

    Learning Practice

  • 2.4

    Modelling

    Modelling involves training an AI model using prepared data to enable it to make predictions or decisions.

    Learning Practice

  • 2.5

    Evaluation

    The Evaluation stage of the AI Project Cycle assesses the performance and reliability of AI models using various metrics.

    Learning Practice

  • 2.1.1

    Definition

    Problem scoping is the foundational step of the AI Project Cycle, focusing on clearly defining the problem and its boundaries.

    Learning Practice

  • 2.1.2

    Steps In Problem Scoping

    Problem Scoping is the initial stage of the AI Project Cycle, focusing on defining the problem to be solved and its boundaries.

    Learning Practice

  • 2.1.3

    Tools Used

    This section discusses the essential tools utilized in the Problem Scoping stage of the AI Project Cycle.

    Learning Practice

  • 2.2.1

    Definition

    The section provides an overview of the stages involved in the AI Project Cycle, highlighting the importance of structured development for AI systems.

    Learning Practice

  • 2.2.2

    Types Of Data

    This section explores the various types and sources of data crucial for AI projects.

    Learning Practice

  • 2.2.3

    Sources Of Data

    This section outlines the various sources of data crucial for AI projects, including types of data and considerations for data acquisition.

    Learning Practice

  • 2.2.4

    Considerations

    The Considerations section highlights crucial factors in data acquisition for AI projects, focusing on data relevance, accuracy, and ethics.

    Learning Practice

  • 2.3.1

    Definition

    Data Exploration involves analyzing collected data to uncover useful patterns, clean errors, and gain a deep understanding of the data.

    Learning Practice

  • 2.3.2

    Key Tasks

    This section outlines the key tasks involved in Data Exploration within the AI Project Cycle.

    Learning Practice

  • 2.3.3

    Why It's Important

    Data exploration is essential as it prepares the dataset for training an AI model, affecting the model's performance.

    Learning Practice

  • 2.4.1

    Definition

    This section defines each stage of the AI Project Cycle, highlighting the importance of a structured approach in developing AI systems.

    Learning Practice

  • 2.4.2

    Steps In Modelling

    The modelling stage in the AI Project Cycle involves training an AI model using prepared data to make predictions or decisions.

    Learning Practice

  • 2.4.3

    Types Of Ai Models

    This section discusses various types of AI models, highlighting their purposes and applications.

    Learning Practice

  • 2.5.1

    Definition

    This section defines the fundamental concepts involved in the AI Project Cycle, emphasizing the importance of structured stages in AI development.

    Learning Practice

  • 2.5.2

    Metrics Used

    The metrics used in the Evaluation phase of the AI Project Cycle are essential for assessing an AI model's performance.

    Learning Practice

  • 2.5.3

    Why It's Important

    The importance of evaluating AI models lies in ensuring their reliability and effectiveness before deployment.

    Learning Practice

  • 2.5.4

    Real-Life Example: Ai In Healthcare

    This section showcases the application of the AI Project Cycle in developing an AI model for pneumonia detection in healthcare.

    Learning Practice

  • 2.6

    Summary

    The AI Project Cycle outlines the structured process of developing AI systems, emphasizing the importance of each stage from problem scoping to evaluation.

    Learning Practice

References

u1ch2.pdf

Class Notes

Memorization

What we have learnt

  • The AI Project Cycle compri...
  • Problem scoping defines the...
  • Data exploration ensures th...

Final Test

Revision Tests