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Average Ratings 0 Ratings

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features
design
support

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Write a Review

Description

Amazon SageMaker Data Wrangler significantly shortens the data aggregation and preparation timeline for machine learning tasks from several weeks to just minutes. This tool streamlines data preparation and feature engineering, allowing you to execute every phase of the data preparation process—such as data selection, cleansing, exploration, visualization, and large-scale processing—through a unified visual interface. You can effortlessly select data from diverse sources using SQL, enabling rapid imports. Following this, the Data Quality and Insights report serves to automatically assess data integrity and identify issues like duplicate entries and target leakage. With over 300 pre-built data transformations available, SageMaker Data Wrangler allows for quick data modification without the need for coding. After finalizing your data preparation, you can scale the workflow to encompass your complete datasets, facilitating model training, tuning, and deployment in a seamless manner. This comprehensive approach not only enhances efficiency but also empowers users to focus on deriving insights from their data rather than getting bogged down in the preparation phase.

Description

With Amazon SageMaker Pipelines, you can effortlessly develop machine learning workflows using a user-friendly Python SDK, while also managing and visualizing your workflows in Amazon SageMaker Studio. By reusing and storing the steps you create within SageMaker Pipelines, you can enhance efficiency and accelerate scaling. Furthermore, built-in templates allow for rapid initiation, enabling you to build, test, register, and deploy models swiftly, thereby facilitating a CI/CD approach in your machine learning setup. Many users manage numerous workflows, often with various versions of the same model. The SageMaker Pipelines model registry provides a centralized repository to monitor these versions, simplifying the selection of the ideal model for deployment according to your organizational needs. Additionally, SageMaker Studio offers features to explore and discover models, and you can also access them via the SageMaker Python SDK, ensuring versatility in model management. This integration fosters a streamlined process for iterating on models and experimenting with new techniques, ultimately driving innovation in your machine learning projects.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
Amazon Athena Yes 
Amazon EMR Yes 
Amazon Redshift Yes 
Amazon S3 Yes 
Amazon SageMaker Feature Store Yes 
Amazon SageMaker Studio Yes 
Amazon SageMaker Unified Studio Yes 
Apache Parquet Yes 
Apache Spark Yes 
Databricks Yes 
Facebook Ads Yes 
Google Analytics Yes 
JSON Yes 
Meta Ads Yes 
PySpark Yes 
SAP Cloud Platform Yes 
Salesforce Yes 
pandas Yes 

Integrations

Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
Amazon Athena No 
Amazon EMR No 
Amazon Redshift No 
Amazon S3 No 
Amazon SageMaker Feature Store No 
Amazon SageMaker Studio No 
Amazon SageMaker Unified Studio No 
Apache Parquet No 
Apache Spark No 
Databricks No 
Facebook Ads No 
Google Analytics No 
JSON No 
Meta Ads No 
PySpark No 
SAP Cloud Platform No 
Salesforce No 
pandas No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/sagemaker/data-wrangler/

Vendor Details

Company Name

Amazon

Founded

2006

Country

United States

Website

aws.amazon.com/sagemaker/pipelines/

Product Features

Data Preparation

Collaboration Tools No 
Data Access No 
Data Blending No 
Data Cleansing No 
Data Governance No 
Data Mashup No 
Data Modeling No 
Data Transformation No 
Machine Learning No 
Visual User Interface No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Continuous Delivery

Application Lifecycle Management No 
Application Release Automation No 
Build Automation No 
Build Log No 
Change Management No 
Configuration Management No 
Continuous Deployment No 
Continuous Integration No 
Feature Toggles / Feature Flags No 
Quality Management No 
Testing Management No 

Continuous Integration

Build Log No 
Change Management No 
Configuration Management No 
Continuous Delivery No 
Continuous Deployment No 
Debugging No 
Permission Management No 
Quality Assurance Management No 
Testing Management No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

Alternatives

Amazon SageMaker Ground Truth Reviews

Amazon SageMaker Ground Truth

Amazon Web Services