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SMALL
AI Technology Workflow – Step-by-Step Process
This diagram visually represents the fundamental steps involved in developing and deploying an AI model. The process follows a structured approach:
1. Data Collection:
Gather raw data from various sources such as sensors, databases, websites, or user interactions.
Ensure data is relevant and sufficient for the AI task.
2. Data Preprocessing:
Clean and organize data by removing duplicates, handling missing values, and normalizing formats.
Apply techniques like tokenization, stemming, or image resizing, depending on the data type.
3. Data Preparation:
Transform data into a suitable format for training, including feature extraction and encoding.
Split the dataset into training, validation, and test sets for effective learning.
4. Training Data Selection:
Choose the most relevant and high-quality data samples to enhance model performance.
Balance the dataset to prevent bias in the AI system.
5. Algorithm Selection:
Identify and choose the best machine learning or deep learning model for the task (e.g., Decision Trees, Neural Networks, Transformers).
Consider trade-offs between accuracy, speed, and complexity.
6. Training the AI Model:
Feed the selected data into the AI model for learning patterns and relationships.
Optimize model parameters using techniques like backpropagation and gradient descent.
7. Testing and Validation:
Evaluate the trained model using validation and test datasets to measure accuracy and generalization.
Fine-tune hyperparameters to improve performance.
8. Deployment:
Integrate the AI model into real-world applications, such as chatbots, recommendation systems, or autonomous systems.
Ensure scalability and efficiency for handling real-time data.
9. Continuous Monitoring & Data Collection:
Monitor model performance post-deployment to detect issues like concept drift.
Collect new data to retrain and improve the model, ensuring adaptability over time.
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