You work for a sports wagering company as a machine learning specialist.
Your team is responsible for building the machine learning models that produce the sports wager line for the NFL (National Football League) games each week.
You are working on the line versus the spread model.
For this model, you have chosen the XGBoost algorithm.
You have trained your model and deployed it to Amazon SageMaker Hosting Services where you are now ready to send inference requests to your model. You are sending requests to your inference endpoint, but you are seeing that your inferences are failing.
Which of these would NOT be the source of the problem? (Select TWO)
Click on the arrows to vote for the correct answer
A. B. C. D.Answers: A, C.
Option A is correct.
Inference endpoints built using the XGBoost algorithm only support the text/csv, recordio-protobuf, and text/libsvm request formats.
Option B is incorrect.
Inference endpoints built using the XGBoost algorithm only support the text/csv, recordio-protobuf, and text.libsvm request formats.
Your inference request will fail if you serialize your inference request using the text/tsv format.
Option C is correct.
Inference endpoints built using the XGBoost algorithm only support the text/csv, recordio-protobuf, and text/libsvm request formats.
Option D is incorrect.
Inference endpoints built using the XGBoost algorithm only support the text/csv, recordio-protobuf, and text.libsvm request formats.
Your inference request will fail if you serialize your inference request using the application/json format.
Reference:
Please see the Amazon SageMaker developer guide titled Deploy a Model on Amazon SageMaker Hosting Services, the Amazon SageMaker developer guide titled CreateEndpoint, and the Amazon SageMaker developer guide titled Common Data Formats for Inference.
The XGBoost algorithm is a popular algorithm for building machine learning models. In this case, the machine learning model has been trained using XGBoost for predicting the line versus the spread for NFL games. The model has been deployed to Amazon SageMaker Hosting Services, and the next step is to send inference requests to the model.
An inference request is a request for a prediction from a machine learning model. The request includes input data, and the model returns a prediction based on that input data. In this case, the input data will be information about an NFL game, and the model will return a prediction for the line versus the spread.
The issue is that the inferences are failing. There are multiple reasons why inferences could fail, but the question is asking which option would NOT be the source of the problem.
The options given are:
A. You have serialized your inference request in the text/csv format. B. You have serialized your inference request in the text/tsv format. C. You have serialized your inference request in the text/libsvm format. D. You have serialized your inference request in the application/json format.
Serialization is the process of converting data into a format that can be transmitted over a network or stored in a file. In the case of a machine learning model, serialization refers to the process of converting input data into a format that the model can understand.
Option A, text/csv format, is a commonly used format for serializing input data for machine learning models. It stands for "comma-separated values" and consists of a file where each row represents a data point and each column represents a feature. The values in each cell are separated by commas. This format is commonly used because it is easy to work with and many machine learning frameworks support it.
Option B, text/tsv format, is similar to the text/csv format, but the values in each cell are separated by tabs instead of commas. This format is also commonly used for serializing input data for machine learning models.
Option C, text/libsvm format, is a format used specifically for serializing sparse data for machine learning models. It is used when the data has many features, but most of the features are zero for most of the data points. In this format, the data is represented as a sparse matrix where only the non-zero values are stored.
Option D, application/json format, is a format commonly used for transmitting data between applications. It is a lightweight format that is easy to read and write for both humans and machines. It is also widely supported by many programming languages and frameworks.
So, which options would NOT be the source of the problem? Options A, B, and C are all valid formats for serializing input data for machine learning models, including the XGBoost algorithm. Therefore, they are not the source of the problem.
Option D, application/json format, is also a valid format for serializing input data for machine learning models. Therefore, it is also not the source of the problem.
Thus, the correct answer is that the problem is not caused by any of the given options.