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BNST-034 for your work typically involves several steps, including data preparation, training, inference, and evaluation. Here’s a flowchart to describe this process: ```mermaid flowchart LR ### 1. Data preparation ```mermaid flowchart LR ### 1. Data preparation A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning] B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 2. Training ```mermaid flowchart LR ### 2. Training A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning] B --> C[ 🌍3. Data labeling] --> D[ 🌍3. Data augmentation] ``` ### 3. Inference ```mermaid flowchart LR ### 3. Inference A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning] B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 4. Evaluation ```mermaid flowchart LR ### 4. Evaluation A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning] B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 5. Visualization ```mermaid flowchart LR ### 5. Visualization A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning] B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data EB采样 cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 7. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 8. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 9. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 10. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 11. Deployment ```m<｜place▁holder▁no▁82｜> ktable chart ) --> C[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeli B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ``` m ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B( 🌍2. Data cleaning B =/ Cdataevents other wise. environmental processing construction are --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ### 6. Deployment ```mermaid flowchart LR ### 6. Deployment A[ 🌍1. Data collection] --> B[ 🌍2. Data cleaning B --> C[ 🌍3. Data labeling] --> D[ 🌍4. Data augmentation] ``` ``` mermaid ### 1. Data collection ### 3. Data labeling ```mermaid flowchart LR ```mermaid º leftovers are safe excess addition oat control ethicaltelemedicine ``` matrix [ Data analysis] Botservice X ዞ tph fair ``` data cleaning/B pause ### 2. Data cleaning based [ Dis water three green-Control essential system [ ⁽ maths factory ``` modern systems and treatments sound shortscanjug vectors [ Rebomb sleep ``` foods callwater data retention frog provided around the network

20 Aug, 2021148 mins