What's included

• Model Development

  Algorithm/Model Selection: Select the most appropriate machine learning or deep learning algorithms for the AI agent (e.g., NLP models, reinforcement learning, etc.). Model Training: Train the AI model on the processed data, optimizing it for the defined tasks. Model Evaluation: Test the model using appropriate evaluation metrics (e.g., accuracy, precision, recall, F1 score) and refine as necessary. 4. AI Agent Development

• AI Agent Development

  Integration of AI Model: Incorporate the trained model into a functional agent that can interact with users or systems. Natural Language Processing (NLP) Integration: If applicable, enable the agent to process and understand user input (e.g., via text or speech) using NLP techniques. Actionable Outputs: Implement the decision-making logic for the agent to perform tasks based on its inputs and model predictions. User Interface (UI) Design: Create an intuitive interface (if needed) for users to interact with the AI agent.