Amazon SageMaker offers built-in tools and capabilities for creating machine learning pipelines that incorporate feedback loops. Since machine learning pipelines were covered in Chapter 8, Creating Machine Learning Inference Pipelines, here, we will focus on the significance of incorporating a feedback loop. Let's begin:
- Install the relevant Python packages and set the locations for the training, validation, and model outputs on the S3 bucket, as follows:
!pip install pyarrow!pip install joblib!pip install xgboost#Read the dataset from S3 buckets3_bucket = 'ai-in-aws's3_prefix = 'Click-Fraud's3_train_prefix = os.path.join(s3_prefix, 'train')s3_val_prefix = os.path.join(s3_prefix, 'val')s3_output_prefix = os.path.join(s3_prefix, ...
