AI System Engineering Task
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An AI System Engineering Task is a software engineering task that produces engineered AI systems.
- Context:
- It can (typically) be performed by an AI Engineer.
- It can (often) involve designing, developing, and deploying AI models, algorithms, and systems, ensuring that they function effectively in real-world applications.
- It can (often) include AI Engineering Stages, such as:
- AI Evaluation, including defining appropriate AI Evaluation Measures to assess model performance.
- AI Evaluation Dataset Setup to create and maintain datasets for testing and validating AI models before production.
- AI System Design, which focuses on the architecture and structure of AI systems to ensure they meet performance, scalability, and reliability requirements.
- AI System Development, which involves building and training AI models using various machine learning and deep learning techniques.
- AI System Deployment, the process of integrating AI models into production systems, ensuring they are ready for real-world use.
- AI System Monitoring and AI Model Maintenance, which involves tracking performance in production environments and updating models to adapt to new data or requirements.
- AI Infrastructure Engineering is used to handle large-scale AI system deployments, ensuring the system can handle high traffic and data loads.
- AI Ethics and Fairness Tasks, which address issues related to bias, fairness, transparency, and accountability in AI systems.
- ...
- AI Evaluation, including defining appropriate AI Evaluation Measures to assess model performance.
- ...
- It can require collaboration with Data Scientists to implement AI models into production environments.
- It can involve using an AI platform to support the development lifecycle.
- It can adhere to specific AI Engineering Best Practices to ensure the system's scalability, performance, and accuracy.
- It can be performed within the scope of AI Engineering Projects focused on solving real-world problems using AI solutions.
- ...
- Example(s):
- Machine Learning Engineering Tasks.
- Developing a Computer Vision System to detect and classify images in real-time.
- Implementing a Natural Language Processing System for automated customer service chatbots.
- Deploying a Recommendation System to suggest products based on past behaviors to users.
- ...
- Counter-Example(s):
- Data Engineering Task, which focuses more on building and maintaining data pipelines rather than AI systems.
- Rule-Based System Development Task, which does not involve machine learning or adaptive AI systems.
- See: Machine Learning Engineering Task, AI Model Development Task, Data Science Task