AI Development Practice

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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.
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- 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.
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- 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.
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- 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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