AI Development Practice

An AI Development Practice is a software development practice that can be used to create AI systems (that support AI deployment tasks).

• AKA: AI Engineering Practice, AI System Development Practice, Machine Learning Development Practice.
• Context:
  • It can typically implement AI Development Model Training through AI development training pipelines and AI development experiment tracking.
  • It can typically ensure AI Development Model Validation through AI development test sets and AI development performance metrics.
  • It can typically manage AI Development Data Pipelines through AI development data preprocessing and AI development feature engineering.
  • It can typically establish AI Development Version Control through AI development model registrys and AI development artifact tracking.
  • It can typically maintain AI Development Documentation through AI development model cards and AI development technical specifications.
  • ...
  • It can often enforce AI Development Ethical Standards through AI development bias testing and AI development fairness assessments.
  • It can often support AI Development Reproducibility through AI development environment management and AI development dependency tracking.
  • It can often facilitate AI Development Collaboration through AI development experiment sharing and AI development knowledge transfer.
  • It can often implement AI Development Monitoring through AI development model drift detection and AI development performance tracking.
  • It can often enable AI Development Explainability through AI development interpretability methods and AI development transparency reports.
  • ...
  • It can range from being a Research AI Development Practice to being a Production AI Development Practice, depending on its AI development deployment maturity.
  • It can range from being a Traditional AI Development Practice to being a MLOps AI Development Practice, depending on its AI development automation level.
  • It can range from being a Single-Model AI Development Practice to being an Enterprise AI Development Practice, depending on its AI development scale.
  • It can range from being a Experimental AI Development Practice to being a Regulated AI Development Practice, depending on its AI development compliance requirements.
  • ...
  • It can integrate with AI Development Platforms for AI development infrastructure management.
  • It can utilize AI Development Tools for AI development workflow automation.
  • It can support AI Development Frameworks for AI development standardization.
  • It can align with AI Development Regulations for AI development compliance assurance.
  • ...
• Examples:
  • AI Development Model Lifecycle Practices, such as:
    • AI Model Training Practices, such as:
      • Supervised Learning Development Practice implementing AI development labeled data training.
      • Reinforcement Learning Development Practice utilizing AI development reward-based training.
      • Transfer Learning Development Practice leveraging AI development pre-trained models.
    • AI Model Deployment Practices, such as:
      • AI Model Serving Practice providing AI development inference endpoints.
      • AI Edge Deployment Practice enabling AI development on-device processing.
      • AI Cloud Deployment Practice utilizing AI development scalable infrastructure.
  • AI Development Data Management Practices, such as:
    • AI Data Annotation Practices implementing AI development label quality control.
    • AI Data Versioning Practices maintaining AI development dataset lineage.
    • AI Synthetic Data Practices generating AI development training data augmentation.
    • AI Data Privacy Practices ensuring AI development data protection compliance.
  • AI Development Testing Practices, such as:
    • AI Model Testing Practices, such as:
      • AI Performance Testing Practice measuring AI development accuracy metrics.
      • AI Robustness Testing Practice evaluating AI development adversarial resistance.
      • AI Fairness Testing Practice assessing AI development bias metrics.
    • AI System Testing Practices, such as:
      • AI Integration Testing Practice verifying AI development component interactions.
      • AI Load Testing Practice evaluating AI development scalability limits.
      • AI Safety Testing Practice validating AI development failure modes.
  • AI Development Governance Practices, such as:
    • Responsible AI Development Practice implementing AI development ethical guidelines.
    • AI Development Risk Assessment Practice identifying AI development potential harms.
    • AI Development Compliance Practice ensuring AI development regulatory adherence.
    • AI Development Audit Practice maintaining AI development accountability trails.
  • AI Development Tool-Specific Practices, such as:
    • TensorFlow Development Practices utilizing AI development tensorflow framework.
    • PyTorch Development Practices leveraging AI development pytorch ecosystem.
    • Hugging Face Development Practices implementing AI development transformer models.
    • MLflow Development Practices managing AI development experiment lifecycle.
  • AI Development Specialized Domain Practices, such as:
    • Computer Vision Development Practices, such as:
      • Image Classification Development Practice building AI development visual recognition systems.
      • Object Detection Development Practice creating AI development localization models.
    • Natural Language Processing Development Practices, such as:
      • Language Model Development Practice training AI development text generation systems.
      • Named Entity Recognition Development Practice building AI development entity extraction models.
    • Time Series AI Development Practices implementing AI development forecasting models.
  • AI Development Infrastructure Practices, such as:
    • AI Development Environment Practices, such as:
      • AI Development Container Practice using AI development docker environments.
      • AI Development GPU Cluster Practice managing AI development compute resources.
    • AI Development Pipeline Practices, such as:
      • AI Development CI/CD Practice automating AI development model deployment.
      • AI Development Workflow Orchestration Practice coordinating AI development pipeline stages.
  • Enterprise AI Development Practices, such as:
    • Google AI Development Practice (2018) implementing AI development principles.
    • Microsoft AI Development Practice (2019) ensuring AI development responsible standards.
    • Amazon AI Development Practice (2020) providing AI development service integration.
    • Meta AI Development Practice (2021) advancing AI development research applications.
  • ...
• Counter-Examples:
  • Traditional Software Development Practice, which lacks AI development model training and AI development data pipeline considerations.
  • Data Analysis Practice, which focuses on statistical insights rather than AI development system building.
  • Business Intelligence Practice, which emphasizes reporting dashboards rather than AI development model deployment.
  • Database Administration Practice, which manages data storage without AI development model lifecycle.
• See: Software Development Practice, ML Engineering Practice, Data Science Practice, AI Framework, MLOps Practice, AI Safety Practice, Responsible AI Development Practice, AI Testing Framework.

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