title: How to custom code an inference pipeline in AWS sagemaker?
body: <p>I am building a time series usecase to automate the preprocess and retrain tasks.At first the data is preprocessed using numpy, pandas, statsmodels etc &amp; later a machine learning algorithm is applied to make predictions.
  The reason for using inference pipeline is that it reuses the same preprocess code for training and inference. I have checked the examples given by AWS sagemaker team with spark and sci-kit learn. In both the examples they use a sci-kit learn container to fit &amp; transform their preprocess code. Should I also have to create a container which is not needed in my use case as I am not using any sci-kit-learn code? </p>
  
  <p>Can someone give me a custom example of using these pipelines? Any help is appreciated!</p>
  
  <p><strong>Sources looked into:</strong></p>
  
  <p><a href="https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/scikit_learn_inference_pipeline" rel="nofollow noreferrer">https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/scikit_learn_inference_pipeline</a>
  <a href="https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/inference_pipeline_sparkml_blazingtext_dbpedia" rel="nofollow noreferrer">https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/inference_pipeline_sparkml_blazingtext_dbpedia</a></p>