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Sentiment Analysis using Dell APEX File Storage for AWS and Amazon SageMaker
Executive summary Solution Architecture Solution Overview Solution Validation
Solution Validation
Step 1: Amazon SageMaker Studio
Step 2: Setup and import the IMDB dataset
Step 3: Create the Training, Testing, and Validation sets
Step 4: Select the BERT preprocessing module
Step 5: Pass data to the preprocessing module and BERT
Step 6: Pass data to the preprocessing module and BERT
Step 7: Training and Evaluation
Step 8: Saving and Re-loading the model.
Step 9: Predictions
Conclusion References

Step 9: Predictions

Home > APEX > Storage > White Papers > Sentiment Analysis using Dell APEX File Storage for AWS and Amazon SageMaker > Step 9: Predictions

Step 9: Predictions

  • If you want to use the model on TF Serving, you  can call it using SavedModel through one of its named signatures. In Python, you can test it as follows: