About
Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data you want from a wide variety of data sources and import it quickly. Next, you can use the Data Quality and Insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly transform data without writing any code. Once you have completed your data preparation workflow, you can scale it to your full datasets using SageMaker data processing jobs; train, tune, and deploy models.
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About
Using Amazon SageMaker Pipelines, you can create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly. Many customers have hundreds of workflows, each with a different version of the same model. With the SageMaker Pipelines model registry, you can track these versions in a central repository where it is easy to choose the right model for deployment based on your business requirements. You can use SageMaker Studio to browse and discover models, or you can access them through the SageMaker Python SDK.
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About
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately.
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About
Automate the continuous delivery of change through your environments via GitOps and create previews on pull requests to help you accelerate. Rather than having to have deep knowledge of Kubernetes, containers, or Tekton, Jenkins X will automate awesome Tekton pipelines for your projects that fully implement CI and CD which you can manage via GitOps. Each team gets a set of environments. Jenkins X then automates the management of the environments and the promotion of new versions of applications between environments via GitOps and pull requests. Jenkins X automatically spins up preview environments for your pull requests so you can get fast feedback before changes are merged to the main branch. Jenkins X automatically comments on your commits, issues, and pull requests with feedback as code is ready to be previewed, is promoted to environments, or if pull requests are generated automatically to upgrade versions.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Professionals wanting a solution to select and understand data insights, and transform data to prepare it for ML
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Audience
Individuals that need a first purpose-built CI/CD service for machine learning
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Audience
Companies looking for a tool to learn and experiment with ML using a no-setup, free development environment
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Audience
Teams and developers interested in a solution to accelerate their continuous delivery on Kubernetes
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationAmazon
Founded: 1994
United States
aws.amazon.com/sagemaker/data-wrangler/
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Company InformationAmazon
Founded: 2006
United States
aws.amazon.com/sagemaker/pipelines/
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Company InformationAmazon
Founded: 1994
United States
aws.amazon.com/sagemaker/studio-lab/
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Company InformationThe Linux Foundation
United States
jenkins-x.io
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Categories |
Categories |
Categories |
Categories |
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Integrations
Amazon Athena
Amazon Redshift
Amazon S3
Amazon SageMaker Feature Store
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Apache Parquet
Conda
Databricks Data Intelligence Platform
Facebook Ads
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Integrations
Amazon Athena
Amazon Redshift
Amazon S3
Amazon SageMaker Feature Store
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Apache Parquet
Conda
Databricks Data Intelligence Platform
Facebook Ads
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Integrations
Amazon Athena
Amazon Redshift
Amazon S3
Amazon SageMaker Feature Store
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Apache Parquet
Conda
Databricks Data Intelligence Platform
Facebook Ads
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Integrations
Amazon Athena
Amazon Redshift
Amazon S3
Amazon SageMaker Feature Store
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Apache Parquet
Conda
Databricks Data Intelligence Platform
Facebook Ads
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