Using DNN Embedding Models in PySpark at AdTech Scale
📰 Medium · Data Science
Learn to use DNN embedding models in PySpark for large-scale AdTech applications and improve inference speed on billions of records
Action Steps
- Build a DNN embedding model using PyTorch
- Convert the model to ONNX format for compatibility
- Integrate the ONNX model with PySpark for distributed inference
- Configure PySpark to handle large-scale data processing
- Test the model on a sample dataset to ensure correctness
Who Needs to Know This
Data scientists and engineers working on AdTech projects can benefit from this knowledge to improve the performance and scalability of their models
Key Insight
💡 Using DNN embedding models in PySpark can significantly improve inference speed and scalability for large-scale AdTech applications
Share This
⚡️ Speed up inference on billions of records with DNN embedding models in PySpark! #AdTech #PySpark #DNN
Key Takeaways
Learn to use DNN embedding models in PySpark for large-scale AdTech applications and improve inference speed on billions of records
Full Article
Inferencing on billions of records with PyTorch and ONNX Continue reading on Medium »
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