model_data = pd.get_dummies(data) # Convert categorical variables to sets of indicators model_data model_data = model_data.drop(['duration', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed'], axis=1) train_data, validation_data, test_data = np....
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For example, choosing Calendar Day for Frequency value and 1 for Rate sets the interval to increase every 1 calendar day, such as 2023-03-26 00:00:00, 2023-03-27 00:00:00, 2023-03-28 00:00:00. See the table after this procedure for a complete list of Frequency value. Choose ...
Train layer 2 BERTopic model– Another SageMaker TrainingStep is used to train the second layer of the BERTopic model using an ECR image and a custom training script. estimator_second_layer=Estimator(image_uri=container_image_uri,instance_type=training_instance_type,# sa...
variable to capture when pdays takes a value of 999data["not_working"]=np.where(np.in1d(data["job"],["student","retired","unemployed"]),1,0)# Indicator for individuals not actively employedmodel_data=pd.get_dummies(data)# Convert categorical variables to sets of indicatorsmodel_data ...
After the model has been fine-tuned and imported into Amazon Bedrock, you can experiment by sending different sets of input questions and context to the model to generate a response, as shown in the following example: question: """How did Amazon's international segment operating income ch...
After the model has been fine-tuned and imported into Amazon Bedrock, you can experiment by sending different sets of input questions and context to the model to generate a response, as shown in the following example: question: """How did Amazon's international segment operating income ch...
OpenAI'sChatGPT API, which launched in March 2023, let enterprise users opt out of having their data used to train models. But Amazon Bedrock lets users privately train their own customizable instances of foundational models, providing control over which data sets are used. That data is encrypte...
Create a hyperparameter optimization (HPO) tuning job with parameter ranges and pass the train and validation sets as parameters to the function. hyperparameter_ranges = {'iterations': IntegerParameter(80000, 130000), 'max_depth': IntegerParameter(6, 10), 'max...
The following code example sets static values for theeval_metric,num_round,objective,rate_drop, andtweedie_variance_powerparameters of theXGBoost algorithm with Amazon SageMaker AIbuilt-in algorithm. anchoranchor fromsagemaker.amazon.amazon_estimatorimportget_image_uri training_image = get_image_uri(reg...