Cloudera

JANUARY 30, 2019

Tensorflow, PyTorch, or XGBoost) within isolated project environments. With Cloudera Data Science Workbench, data scientists can: Use R, Python, or Scala along with the scale-out processing capabilities of Apache Spark 2.X X on HDP clusters from a web browser, with no desktop footprint. Utilize GPUs effectively for workload specific needs.

Data Science 72

Data Science Machine Learning Experimentation Cost-Benefit 72

Cloudera

APRIL 30, 2021

In this example, CDE is used to execute a Machine learning scoring job that is dependent on packages such as pandas, NumPy, XGBoost, and more using a custom container. To deploy this example, follow these steps – Get the base image name & tag from the Cloudera docker repository. Login to the Cloudera Docker Repo.

Management 61

Management Data Processing Machine Learning Enterprise 61

Domino Data Lab

MAY 1, 2019

Mawer’s interactive “Parameters and Settings” slide has been extracted and reformatted below to ease readability. test_size: 0.25 validate_size: 0.25 Mawer’s interactive “Parameters and Settings” slide has been extracted and reformatted below to ease readability. test_size: 0.25 validate_size: 0.25

Machine Learning 81

Machine Learning Testing Data Science Modeling 81

DataRobot

JULY 20, 2021

With reference to the results table below, our XGBoost and lightGBM models achieved near-perfect accuracy. Third , for future work, we can consider using anomaly detection on only the benign samples, and multi-classification on behavioral tags (e.g., This is about the same AUC accuracy level (0.997-0.998) as in the SoReL-20M paper.

Machine Learning 52

Machine Learning Testing Modeling Software 52