You have successfully trained a DNN regressor gcp video
ml-engineer-pro video for you have successfully trained a DNN regressor using TensorFlow to predict housing prices, utilizing a set of predictive features. The
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You have successfully trained a DNN regressor using TensorFlow to predict housing prices, utilizing a set of predictive features. The default precision for your model is tf.float64, and you've employed a standard TensorFlow estimator with the following configuration: estimator = tf . estimator . DNNRegressor ( feature_columns =[ YOUR_LIST_OF_FEATURES ], hidden_units =[ 1024 , 512 , 256 ], dropout = None ) Your model's performance is satisfactory; however, as you prepare to deploy it into production, you notice that your current serving latency on CPUs is 10ms at the 90th percentile. Your production requirements dictate a model latency of 8ms at the 90th percentile, and you are open to a slight decrease in prediction performance to meet this latency requirement. To achieve this, what should be your initial approach to quickly reduce the serving latency?