AWS Machine Learning Blog

AUGUST 22, 2023

We’re excited to announce Amazon SageMaker Data Wrangler support for Amazon S3 Access Points. Solution Overview Imagine you, as an administrator, have to manage data for multiple data science teams running their own data preparation workflows in SageMaker Data Wrangler.

AWS 97

AWS Data Science Data Preparation Artificial Intelligence 97

AWS Machine Learning Blog

SEPTEMBER 3, 2024

With the introduction of EMR Serverless support for Apache Livy endpoints , SageMaker Studio users can now seamlessly integrate their Jupyter notebooks running sparkmagic kernels with the powerful data processing capabilities of EMR Serverless. elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*" elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*"

AWS 121

AWS Clustering Big Data Big Data 121

Towards AI

FEBRUARY 18, 2024

This historical sales data covers sales information from 2010–02–05 to 2012–11–01. So let’s filter out and keep only a handful of data to perform the analysis. Data Preparation It’s time me filter out the unnecessary records to make it easier to visualize the dataset. df['Store'] = df['Store'].astype('category')df['Dept']

Python 115

Python Data Preparation Algorithm AI 115

Flipboard

MARCH 7, 2023

Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity. Check that the SageMaker image selected is a Conda-supported first-party kernel image such as “Data Science.” Choose Open Launcher.

Python 123

Python AWS ML ML 123

DataRobot

SEPTEMBER 14, 2021

Since DataRobot was founded in 2012, we’ve been committed to democratizing access to the power of AI. We’re building a platform for all users: data scientists, analytics experts, business users, and IT. Let’s dive into each of these areas and talk about how we’re delivering the DataRobot AI Cloud Platform with our 7.2

AI 52

AI AI Data Pipeline Data Preparation 52

AWS Machine Learning Blog

AUGUST 4, 2023

Train a recommendation model in SageMaker Studio using training data that was prepared using SageMaker Data Wrangler. The real-time inference call data is first passed to the SageMaker Data Wrangler container in the inference pipeline, where it is preprocessed and passed to the trained model for product recommendation.

ML 98

ML ML AWS AI 98

Flipboard

AUGUST 17, 2023

If you are prompted to choose a kernel, choose Data Science as the image and Python 3 as the kernel, then choose Select. Option C: Use SageMaker Data Wrangler SageMaker Data Wrangler allows you to import data from various data sources including Amazon Redshift for a low-code/no-code way to prepare, transform, and featurize your data.

ML 123

ML ML AWS Data Warehouse 123