AI Software Engineering & Development Process
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Collaborating with clients to clearly define AI application scenarios, such as Computer Vision (CV), Speech Recognition, Natural Language Processing (NLP), and Recommendation Systems.
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Reviewing and gathering available datasets to verify data quality, volume, and annotation status.
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Performing data cleaning, noise reduction, and missing value imputation.
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Establishing feature engineering pipelines to enhance model learnability and predictive accuracy.
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Selecting appropriate architectures based on application requirements, such as CNN, RNN, Transformer, or LLM.
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Conducting model design and hyperparameter planning to ensure an optimal balance between performance and resource utilization.
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Utilizing GPU clusters and AI servers for large-scale model training.
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Continuously monitoring loss functions and key performance metrics (such as Accuracy, Recall, and F1 Score).
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Executing cross-validation and iterative tuning to ensure robust model generalization.
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Establishing MLOps pipelines to automate training, deployment, and monitoring processes.
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Periodically updating models to adapt to evolving data trends and market demands.
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Providing performance monitoring and anomaly detection to ensure long-term system stability and reliability.
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Encapsulating models into APIs or microservices for integration into client systems.
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Supporting multi-environment deployment across cloud, edge devices, or on-premises servers.
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Achieving seamless integration with existing IT infrastructure and business workflows.
Requirements Analysis & Data Inventory
Data Preprocessing & Feature Engineering
Model Design & Architecture Selection
Model Training & Validation
Deployment & System Integration
Maintenance & Continuous Optimization
Innovation ｜Warmness ｜Artificial Intelligence