AWS Machine Learning

MARCH 20, 2023

Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. In this case, you are calibrating the model with the SQuAD dataset: model.eval() conf = ipex.quantization.QuantConf(qscheme=torch.per_tensor_affine) print("Doing calibration.")

Calibration 77

Calibration Scripts Benchmark APIs 77

AWS Machine Learning

JANUARY 17, 2023

We now carry out feature engineering steps and then fit the model. The model training consists of two components: a feature engineering step that processes numerical, categorical, and text features, and a model fitting step that fits the transformed features into a Scikit-learn random forest classifier. BERT + Random Forest.

Calibration 69

Calibration Metrics Accountability Technical Support 69

Callminer

AUGUST 1, 2017

Metrics, Measure, and Monitor – Make sure your metrics and associated goals are clear and concise while aligning with efficiency and effectiveness. Make each metric public and ensure everyone knows why that metric is measured. Interactive agent scripts from Zingtree solve this problem. Bill Dettering.

Contact Center 334

Contact Center Call Center Average Handle Time Real estate 334

AWS Machine Learning

APRIL 3, 2023

The causal inference engine is deployed with Amazon SageMaker Asynchronous Inference. The database was calibrated and validated using data from more than 400 trials in the region. The following figure illustrates these metrics. For further details, refer to the feature extraction script.

Calibration 80

Calibration Engineering Scripts Accountability 80

AWS Machine Learning

OCTOBER 19, 2022

Lastly, we compare the classification result with the ground truth labels and compute the evaluation metrics. Because our dataset is imbalanced, we use the evaluation metrics balanced accuracy , Cohen’s Kappa score , F1 score , and ROC AUC , because they take into account the frequency of each class in the data. Balanced accuracy.

APIs 66

APIs Metrics Calibration Accountability 66

AWS Machine Learning

NOVEMBER 29, 2023

Furthermore, these data and metrics must be collected to comply with upcoming regulations. They need evaluation metrics generated by model providers to select the right pre-trained model as a starting point. Evaluating these models allows continuous model improvement, calibration and debugging.

Benchmark 79

Benchmark Metrics Engineering APIs 79

AWS Machine Learning

APRIL 11, 2023

When processing large-scale data, data scientists and ML engineers often use PySpark , an interface for Apache Spark in Python. SageMaker provides prebuilt Docker images that include PySpark and other dependencies needed to run distributed data processing jobs, including data transformations and feature engineering using the Spark framework.

Calibration 72

Calibration Scripts Engineering Best practices 72