Slides - Link
Key Notes
- Discovery Phase
- Model building Phase
- Deploy Phase
- Discover - Develop - Deploy
- Develop - Iterate - Validate - Deploy
- Monitoring / Reliability / Highly Available Services
- Scan and Fetch details
- Fetch information / Details from product
- Prototype built on core ios
- Platform independent architecture
- Robust pipeline
- Build new models
- Mobilenet Transfer Learning architecture
- Object texture / size / shape
- Handmade products with variants
- Product shape degrades over time
- Lighting/occlusion challenges
- Data Augmentation / Randomization
- Pretrained weights on imagenet
- Model F1 Score Accuracy
- Model Pipeline implementation
- Building cross functional teams
- Devops Tools mastering
- ML Tools
- Custom and Tailored solutions from GCP
- Own custom solution to productization
- Model Factory
- Preprocessing
- Automation / Components
- Model Validation
- Model Versioning
- Tensorflowlite for cross device deployment
- Small model footprint
- Storage
- Power consumption of APP
- Model in Cloud
- Image in object Storage
- Data Augmentation Pipeline
- Playing with color distribution / Saturation
- Python pipeline
- Apache beam
- Streaming API
- Batch processing
- Cloud dataflow execution engine
- Automated Training Pipeline
- Automated Deployment Pipeline
- Metrics storage in bigquery
- Save Every image in call
- Lower precision on Inference (Int8)
- During training, we can keep precision
- Google Cloud Composer
- Run Airflow for VM
- Build custom workflows
- Airflow to orchestrate data tasks
- Write each tasks in python
- Monitoring / Alerting
- Failure handing / Notification
Happy Learning!!!
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