The AI project cycle is a framework that can be used to develop and deploy AI solutions. It consists of a series of steps that are designed to ensure that AI projects are successful and meet the needs of the users.
The AI project cycle can be divided into the following stages:
- Problem scoping: This stage involves identifying the problem that the AI solution is intended to solve. It is important to have a clear understanding of the problem, as this will inform the rest of the project cycle.
- Data acquisition: Once the problem has been scoped, the next step is to acquire the data that will be used to train and evaluate the AI model. The data should be high quality and relevant to the problem that is being solved.
- Data exploration: Once the data has been acquired, it needs to be explored and cleaned. This involves identifying and removing any outliers or errors in the data. It is also important to understand the distribution of the data and to identify any features that may be important for the AI model.
- Feature engineering: Feature engineering is the process of creating new features from the existing data. This can be done to improve the performance of the AI model or to make it more interpretable.
- Model building: Once the data has been prepared, the next step is to build the AI model. This involves selecting an appropriate machine learning algorithm and training the model on the data.
- Model evaluation: Once the AI model has been trained, it needs to be evaluated on a held-out test set. This helps to assess the performance of the model and to identify any areas where it can be improved.
- Model deployment: Once the AI model has been evaluated and is performing well, it can be deployed to production. This involves making the model available to users so that they can use it to solve the problem that it was designed to address.
Steps in the AI Project Cycle
1. Problem Scoping
The first step in the AI project cycle is to identify the problem that the AI solution is intended to solve. It is important to have a clear understanding of the problem, as this will inform the rest of the project cycle.
When scoping the problem, it is important to consider the following:
- Who is the target user of the AI solution?
- What are their needs and pain points?
- What are the business goals of the AI solution?
- What are the constraints on the AI solution, such as budget, time, and resources?
Once the problem has been scoped, it is important to define a clear objective for the AI solution. This objective should be specific, measurable, achievable, relevant, and time-bound.
2. Data Acquisition
Once the problem has been scoped, the next step is to acquire the data that will be used to train and evaluate the AI model. The data should be high quality and relevant to the problem that is being solved.
There are a variety of ways to acquire data for AI projects. Some common sources of data include:
- Internal data, such as customer data, sales data, or operational data.
- External data, such as public datasets, social media data, or sensor data.
- Synthetic data, which is generated using computer programs.
When acquiring data, it is important to consider the following:
- The quality of the data. The data should be accurate, complete, and consistent.
- The relevance of the data. The data should be relevant to the problem that the AI solution is intended to solve.
- The quantity of the data. The amount of data needed will depend on the complexity of the AI model and the size of the problem that is being solved.
3. Data Exploration
Once the data has been acquired, it needs to be explored and cleaned. This involves identifying and removing any outliers or errors in the data. It is also important to understand the distribution of the data and to identify any features that may be important for the AI model.
Data exploration can be done using a variety of tools, such as statistical software or data visualization tools.
4. Feature Engineering
Feature engineering is the process of creating new features from the existing data. This can be done to improve the performance of the AI model or to make it more interpretable.
There are a variety of feature engineering techniques that can be used. Some common techniques include:
- Creating new features that are combinations of existing features.
- Normalizing the data to make it easier for the AI model to learn.
- Imputing missing values in the data.
- Encoding categorical data into numerical data.
5. Model Building
Once the data
WebAI project cycle and stages. Generally, the AI project consists of three main stages: Stage I – Project planning and data collection. Stage II – Design and training of. WebApa itu Project Cycle? sesuai dengan kata "cycle" atau siklus atau bisa diartikan sebagai sebuah proses dalam membuat proyek AI secara utuh. AI Project. Webmenggunakan AI Project Cycle yang terdiri dari 6 tahapan seperti pada gambar 3.1. Gambar 3.1. AI Project Cycle (Sumber: (Silva & Alahakoon, 2022)) Pada gambar 3.1..
Artificial Intelligence Class 9 Unit 2 | AI Project Cycle - Overview
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Artificial Intelligence Project Cycle Overview | Class 9 Unit 2
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What Is Ai Project Cycle, Artificial Intelligence Class 9 Unit 2 | AI Project Cycle - Overview, 50.42 MB, 36:43, 140,865, Magnet Brains, 2021-08-12T13:30:14.000000Z, 2, CBSE Class-X AI Lecture #1 AI Project Cycle [Session 1] - YouTube, 360 x 480, jpg, , 3, what-is-ai-project-cycle
What Is Ai Project Cycle. WebIn this article, we have presented the CDAC AI life cycle for the design, development, and deployment of AI systems and solutions, preceded by a preliminary.
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✔️ Chapter: AI Project Cycle - Unit 2
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✔️ Topics Covered in This Video: Project Cycle of AI - Problem Scoping, Data Acquisition, Data Exploration, Modelling, Evaluation, Test Time
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What Is Ai Project Cycle, Webmenggunakan AI Project Cycle yang terdiri dari 6 tahapan seperti pada gambar 3.1. Gambar 3.1. AI Project Cycle (Sumber: (Silva & Alahakoon, 2022)) Pada gambar 3.1..
CBSE Class-X AI Lecture #1 AI Project Cycle [Session 1] - YouTube - Source: m.youtube.com
AI Project Stages: From Planning to Maintenance | Label Your Data - Source: labelyourdata.com
What is AI Project Cycle? What is the need of an AI Project Cycle? - Technology Point - Source: technologypoint.in
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