AI System Development Process

An AI System Development Process is a software development process that involves the systematic creation, testing, and deployment of artificial intelligence models and systems.

• Context:
  • It can (typically) contain AI Development Tasks.
  • It can (often) require a cross-functional team including data scientists, engineers, and domain experts to collaborate effectively.
  • It can (often) start with AI System Requirement Gathering.
  • It can (often) include a data collection phase where relevant data is gathered, cleaned, and preprocessed to be used in training models.
  • ...
  • It can range from being a Simple AI System Development Process to a Comprehensive AI System Development Process.
  • ...
  • It can involve model selection where various algorithms are considered, and the most appropriate one is chosen based on performance metrics.
  • It can include a model training phase where the chosen model is trained on the prepared data using machine learning or deep learning techniques.
  • It can incorporate model evaluation to assess the performance of the trained model using validation and test datasets.
  • It can involve model optimization techniques such as hyperparameter tuning and regularization to improve model performance.
  • It can have a deployment phase where the trained and validated model is integrated into a production environment.
  • It can involve continuous monitoring and maintenance to ensure the AI system operates as expected and adapts to new data over time.
  • It can include an iteration process where the model is periodically retrained and updated based on new data and changing requirements.
  • ...
• Example(s):
  • an ML System Development Process.
  • an AI Model Development Process project that showcases the end-to-end process from data collection to deployment for a predictive maintenance system.
  • an AI-based Recommendation System Development Process development process that demonstrates the integration of AI into an e-commerce platform to personalize user experiences.
  • ...
• Counter-Example(s):
  • Web App Software Development Processes, ...
  • Hardware Development Processes concerned with designing and manufacturing physical components.
• See: Machine Learning Development, Deep Learning Workflow, Software Development Lifecycle.

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References

2023

• (Johnson et al., 2023) ⇒ Emily Johnson, Mark Anderson, and Sara Lee. (2023). "Best Practices in AI Development: From Data Collection to Deployment." In: AI Journal, Volume 18, Page 123-145. doi:xxxxxxx
  • QUOTE: "The paper outlines a comprehensive framework for AI development, emphasizing the importance of iterative testing and validation.”
  • NOTE: It provides a detailed framework for AI development emphasizing iterative testing and validation.

2021

• (Williams & Patel, 2021) ⇒ David Williams, and Anita Patel. (2021). "Optimizing AI Pipelines for Real-World Applications." In: Proceedings of the International Conference on AI.
  • QUOTE: "Effective AI development requires optimizing the entire pipeline, from data preprocessing to model deployment.”
  • NOTE: It emphasizes the need to optimize the entire AI development pipeline for real-world applications.

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