AI Technology Workflow

- AI Technology Workflow – Step-by-Step Process

The diagram above visually represents the basic steps involved in developing and deploying AI models. The process follows a structured approach:

• Data Collection: Collect raw data from various sources (e.g., sensors, databases, websites, or user interactions). Ensure that the data is relevant and sufficient for the AI ​​task.
• Data Preprocessing: Clean and organize the data by removing duplicates, handling missing values, and normalizing the format. Apply techniques such as tokenization, stemming, or image resizing based on the data type.
• Data Preparation: Convert the data into a format suitable for training, including feature extraction and encoding. Separate the dataset into training, validation, and test sets for effective learning.
• Training Data Selection: Select the most relevant and high-quality data samples to enhance model performance. Balance the dataset to prevent bias in the AI ​​system.
• Algorithm Selection: Identify and select the machine learning or deep learning model (e.g., decision trees, neural networks, Transformers) that is best suited for the task. Consider the trade-offs between accuracy, speed, and complexity.
• Training AI Models: Feed selected data into AI models to learn patterns and relationships. Optimize model parameters using techniques such as backpropagation and gradient descent.
• Testing and Validation: Evaluate trained models using validation and test datasets to measure accuracy and generalization. Fine-tune hyperparameters to improve performance.
• Deployment: Integrate AI models into real-world applications such as chatbots, recommender systems, or autonomous systems. Ensure scalability and efficiency for processing real-time data.
• Continuous Monitoring and Data Collection: Monitor model performance after deployment to detect issues such as concept drift. Collect new data to retrain and improve models to ensure adaptability over time.