I want you to come up with a Machine Learning model that can do Binary classification.
Here are some information about the dataset:
Name: Amazon reviews
Training samples: 1800000
Number of classes: 2
Longest sequence = 258
Here are some specifications about model:
Can be fine tuning or train from scratch
Should priority inference speed
Training speed: does not matter
Metrics: Accuracy, Precision, AUC
A good machine learning model for binary classification on the Amazon reviews dataset is a Long Short-Term Memory (LSTM) network with a Convolutional Neural Network (CNN) layer. This model can be fine-tuned or trained from scratch depending on your preference. 
LSTMs are great for handling sequential data, such as text data, and the CNN layer can help extract relevant features from the text data. 
For inference speed, you can use the TensorRT library by NVIDIA to optimize the model for deployment on GPUs. 
In terms of metrics, you can use accuracy, precision, and AUC to evaluate the performance of the model. During training, you can use binary cross-entropy loss as the loss function to optimize.
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:

I want you to come up with a Machine Learning model that can do Binary classification.
Here are some information about the dataset:
Name: Amazon reviews
Training samples: 1800000
Number of classes: 2
Longest sequence = 258
Here are some specifications about model:
Can be fine tuning or train from scratch
Should priority inference speed
Training speed: does not matter
Metrics: Accuracy, Precision, AUC

### Response:

A good machine learning model for binary classification on the Amazon reviews dataset is a Long Short-Term Memory (LSTM) network with a Convolutional Neural Network (CNN) layer. This model can be fine-tuned or trained from scratch depending on your preference. 
LSTMs are great for handling sequential data, such as text data, and the CNN layer can help extract relevant features from the text data. 
For inference speed, you can use the TensorRT library by NVIDIA to optimize the model for deployment on GPUs. 
In terms of metrics, you can use accuracy, precision, and AUC to evaluate the performance of the model. During training, you can use binary cross-entropy loss as the loss function to optimize.
