Amazon SageMaker Ground Truth
As seen on DataEthics4All, Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. Get started with labeling your data in minutes through the SageMaker Ground Truth
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Product Overview.
SageMaker Ground Truth – Training data
Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows. These workflows support a variety of use cases including 3D point clouds, video, images, and text. As part of the workflows, labelers have access to assistive labeling features such as automatic 3D cuboid snapping, removal of distortion in 2D images, and auto-segment tools to reduce the time required to label datasets. In addition, Ground Truth offers automatic data labeling which uses a machine learning model to label your data.
User Reviews.
AI Society
About the Company.
In 2006, Amazon Web Services (AWS) began offering IT infrastructure services to businesses in the form of web services — now commonly known as cloud computing. One of the key benefits of cloud computing is the opportunity to replace up-front capital infrastructure expenses with low variable costs that scale with your business. With the Cloud, businesses no longer need to plan for and procure servers and other IT infrastructure weeks or months in advance. Instead, they can instantly spin up hundreds or thousands of servers in minutes and deliver results faster.
Today, Amazon Web Services provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world. With data center locations in the U.S., Europe, Brazil, Singapore, Japan, and Australia, customers across all industries are taking advantage of the following benefits:
AWS offers low, pay-as-you-go pricing with no up-front expenses or long-term commitments. They are able to build and manage a global infrastructure at scale and pass the cost-saving benefits onto you in the form of lower prices. With the efficiencies of our scale and expertise, they have been able to lower our prices on 15 different occasions over the past four years.
AWS provides a massive global cloud infrastructure that allows you to quickly innovate, experiment and iterate. Instead of waiting weeks or months for hardware, you can instantly deploy new applications, instantly scale up as your workload grows, and instantly scale down based on demand. Whether you need one virtual server or thousands, whether you need them for a few hours or 24/7, you still only pay for what you use.
AWS is a language and operating system agnostic platform. You choose the development platform or programming model that makes the most sense for your business. You can choose which services you use, one or several, and choose how you use them. This flexibility allows you to focus on innovation, not infrastructure.
AWS is a secure, durable technology platform with industry-recognized certifications and audits: PCI DSS Level 1, ISO 27001, FISMA Moderate, FedRAMP, HIPAA, and SOC 1 (formerly referred to as SAS 70 and/or SSAE 16) and SOC 2 audit reports. Our services and data centers have multiple layers of operational and physical security to ensure the integrity and safety of your data.
Other Products.
Amazon SageMaker Studio: Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. Accelerate innovation with purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows.
Amazon SageMaker Autopilot: Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models, and helps you automatically build, train, and tune the best ML model based on your data. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality.
Amazon SageMaker JumpStart: Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. Amazon SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models such as natural language processing, object detection, and image classification models.
Amazon SageMaker Data Wrangler: Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepares 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, cleaning, exploration, and visualization from a single visual interface. Using SageMaker Data Wrangler’s data selection tool, you can choose the data you want from various data sources and import it with a single click. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. With SageMaker Data Wrangler’s visualization templates, you can quickly preview and inspect that these transformations are completed as you intended by viewing them in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines and save them for reuse in the Amazon SageMaker Feature Store.
Amazon SageMaker Feature Store: Amazon SageMaker Feature Store is a purpose-built repository where you can store and access features so it’s much easier to name, organize, and reuse them across teams. SageMaker Feature Store provides a unified store for features during training and real-time inference without the need to write additional code or create manual processes to keep features consistent. SageMaker Feature Store keeps track of the metadata of stored features (e.g. feature name or version number) so that you can query the features for the right attributes in batches or in real-time using Amazon Athena, an interactive query service. SageMaker Feature Store also keeps features updated, because as new data is generated during inference, the single repository is updated so new features are always available for models to use during training and inference.
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Amazon SageMaker Data Wrangler
As seen on DataEthics4All, Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepares data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature
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SageMaker Data Wrangler – Fastest way to prepare data
Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepares 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, cleaning, exploration, and visualization from a single visual interface. Using SageMaker Data Wrangler’s data selection tool, you can choose the data you want from various data sources and import it with a single click. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. With SageMaker Data Wrangler’s visualization templates, you can quickly preview and inspect that these transformations are completed as you intended by viewing them in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines and save them for reuse in the Amazon SageMaker Feature Store.
User Reviews.
AI Society
About the Company.
In 2006, Amazon Web Services (AWS) began offering IT infrastructure services to businesses in the form of web services — now commonly known as cloud computing. One of the key benefits of cloud computing is the opportunity to replace up-front capital infrastructure expenses with low variable costs that scale with your business. With the Cloud, businesses no longer need to plan for and procure servers and other IT infrastructure weeks or months in advance. Instead, they can instantly spin up hundreds or thousands of servers in minutes and deliver results faster.
Today, Amazon Web Services provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world. With data center locations in the U.S., Europe, Brazil, Singapore, Japan, and Australia, customers across all industries are taking advantage of the following benefits:
AWS offers low, pay-as-you-go pricing with no up-front expenses or long-term commitments. They are able to build and manage a global infrastructure at scale and pass the cost-saving benefits onto you in the form of lower prices. With the efficiencies of our scale and expertise, they have been able to lower our prices on 15 different occasions over the past four years.
AWS provides a massive global cloud infrastructure that allows you to quickly innovate, experiment and iterate. Instead of waiting weeks or months for hardware, you can instantly deploy new applications, instantly scale up as your workload grows, and instantly scale down based on demand. Whether you need one virtual server or thousands, whether you need them for a few hours or 24/7, you still only pay for what you use.
AWS is a language and operating system agnostic platform. You choose the development platform or programming model that makes the most sense for your business. You can choose which services you use, one or several, and choose how you use them. This flexibility allows you to focus on innovation, not infrastructure.
AWS is a secure, durable technology platform with industry-recognized certifications and audits: PCI DSS Level 1, ISO 27001, FISMA Moderate, FedRAMP, HIPAA, and SOC 1 (formerly referred to as SAS 70 and/or SSAE 16) and SOC 2 audit reports. Our services and data centers have multiple layers of operational and physical security to ensure the integrity and safety of your data.
Other Products.
Amazon SageMaker Studio: Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. Accelerate innovation with purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows.
Amazon SageMaker Autopilot: Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models, and helps you automatically build, train, and tune the best ML model based on your data. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality.
Amazon SageMaker Ground Truth: Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows. These workflows support a variety of use cases including 3D point clouds, video, images, and text. As part of the workflows, labelers have access to assistive labeling features such as automatic 3D cuboid snapping, removal of distortion in 2D images, and auto-segment tools to reduce the time required to label datasets. In addition, Ground Truth offers automatic data labeling which uses a machine learning model to label your data.
Amazon SageMaker JumpStart: Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. Amazon SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models such as natural language processing, object detection, and image classification models.
Amazon SageMaker Feature Store: Amazon SageMaker Feature Store is a purpose-built repository where you can store and access features so it’s much easier to name, organize, and reuse them across teams. SageMaker Feature Store provides a unified store for features during training and real-time inference without the need to write additional code or create manual processes to keep features consistent. SageMaker Feature Store keeps track of the metadata of stored features (e.g. feature name or version number) so that you can query the features for the right attributes in batches or in real-time using Amazon Athena, an interactive query service. SageMaker Feature Store also keeps features updated, because as new data is generated during inference, the single repository is updated so new features are always available for models to use during training and inference.
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Then fill out the Application to Join the DataEthics4All AI Society today!
Amazon SageMaker Feature Store
As seen on DataEthics4All, Amazon SageMaker Feature Store is a purpose-built repository where you can store and access features so it’s much easier to name, organize, and reuse them across teams. SageMaker Feature Store provides a unified store for features
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Product Overview.
SageMaker Feature Store – A fully managed repository for machine learning features
Amazon SageMaker Feature Store is a purpose-built repository where you can store and access features so it’s much easier to name, organize, and reuse them across teams. SageMaker Feature Store provides a unified store for features during training and real-time inference without the need to write additional code or create manual processes to keep features consistent. SageMaker Feature Store keeps track of the metadata of stored features (e.g. feature name or version number) so that you can query the features for the right attributes in batches or in real-time using Amazon Athena, an interactive query service. SageMaker Feature Store also keeps features updated, because as new data is generated during inference, the single repository is updated so new features are always available for models to use during training and inference.
User Reviews.
AI Society
About the Company.
In 2006, Amazon Web Services (AWS) began offering IT infrastructure services to businesses in the form of web services — now commonly known as cloud computing. One of the key benefits of cloud computing is the opportunity to replace up-front capital infrastructure expenses with low variable costs that scale with your business. With the Cloud, businesses no longer need to plan for and procure servers and other IT infrastructure weeks or months in advance. Instead, they can instantly spin up hundreds or thousands of servers in minutes and deliver results faster.
Today, Amazon Web Services provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world. With data center locations in the U.S., Europe, Brazil, Singapore, Japan, and Australia, customers across all industries are taking advantage of the following benefits:
AWS offers low, pay-as-you-go pricing with no up-front expenses or long-term commitments. They are able to build and manage a global infrastructure at scale and pass the cost-saving benefits onto you in the form of lower prices. With the efficiencies of our scale and expertise, they have been able to lower our prices on 15 different occasions over the past four years.
AWS provides a massive global cloud infrastructure that allows you to quickly innovate, experiment and iterate. Instead of waiting weeks or months for hardware, you can instantly deploy new applications, instantly scale up as your workload grows, and instantly scale down based on demand. Whether you need one virtual server or thousands, whether you need them for a few hours or 24/7, you still only pay for what you use.
AWS is a language and operating system agnostic platform. You choose the development platform or programming model that makes the most sense for your business. You can choose which services you use, one or several, and choose how you use them. This flexibility allows you to focus on innovation, not infrastructure.
AWS is a secure, durable technology platform with industry-recognized certifications and audits: PCI DSS Level 1, ISO 27001, FISMA Moderate, FedRAMP, HIPAA, and SOC 1 (formerly referred to as SAS 70 and/or SSAE 16) and SOC 2 audit reports. Our services and data centers have multiple layers of operational and physical security to ensure the integrity and safety of your data.
Other Products.
Amazon SageMaker Studio: Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. Accelerate innovation with purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows.
Amazon SageMaker Autopilot: Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models, and helps you automatically build, train, and tune the best ML model based on your data. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality.
Amazon SageMaker Ground Truth: Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows. These workflows support a variety of use cases including 3D point clouds, video, images, and text. As part of the workflows, labelers have access to assistive labeling features such as automatic 3D cuboid snapping, removal of distortion in 2D images, and auto-segment tools to reduce the time required to label datasets. In addition, Ground Truth offers automatic data labeling which uses a machine learning model to label your data.
Amazon SageMaker JumpStart: Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. Amazon SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models such as natural language processing, object detection, and image classification models.
Amazon SageMaker Data Wrangler: Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepares 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, cleaning, exploration, and visualization from a single visual interface. Using SageMaker Data Wrangler’s data selection tool, you can choose the data you want from various data sources and import it with a single click. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. With SageMaker Data Wrangler’s visualization templates, you can quickly preview and inspect that these transformations are completed as you intended by viewing them in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines and save them for reuse in the Amazon SageMaker Feature Store.
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Amazon SageMaker Clarify
As seen on DataEthics4All, Amazon SageMaker Clarify detects potential bias during data preparation, after model training, and in your deployed model by examining attributes you specify. For instance, you can check for bias related to age in your initial dataset
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SageMaker Clarify – Detect bias in ML models and understand model predictions
Amazon SageMaker Clarify detects potential bias during data preparation, after model training, and in your deployed model by examining attributes you specify. For instance, you can check for bias related to age in your initial dataset or in your trained model and receive a detailed report that quantifies different types of possible bias. SageMaker Clarify also includes feature importance graphs that help you explain model predictions and produce reports which can be used to support internal presentations or to identify issues with your model that you can take steps to correct.
User Reviews.
AI Society
About the Company.
In 2006, Amazon Web Services (AWS) began offering IT infrastructure services to businesses in the form of web services — now commonly known as cloud computing. One of the key benefits of cloud computing is the opportunity to replace up-front capital infrastructure expenses with low variable costs that scale with your business. With the Cloud, businesses no longer need to plan for and procure servers and other IT infrastructure weeks or months in advance. Instead, they can instantly spin up hundreds or thousands of servers in minutes and deliver results faster.
Today, Amazon Web Services provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world. With data center locations in the U.S., Europe, Brazil, Singapore, Japan, and Australia, customers across all industries are taking advantage of the following benefits:
AWS offers low, pay-as-you-go pricing with no up-front expenses or long-term commitments. They are able to build and manage a global infrastructure at scale and pass the cost-saving benefits onto you in the form of lower prices. With the efficiencies of our scale and expertise, they have been able to lower our prices on 15 different occasions over the past four years.
AWS provides a massive global cloud infrastructure that allows you to quickly innovate, experiment and iterate. Instead of waiting weeks or months for hardware, you can instantly deploy new applications, instantly scale up as your workload grows, and instantly scale down based on demand. Whether you need one virtual server or thousands, whether you need them for a few hours or 24/7, you still only pay for what you use.
AWS is a language and operating system agnostic platform. You choose the development platform or programming model that makes the most sense for your business. You can choose which services you use, one or several, and choose how you use them. This flexibility allows you to focus on innovation, not infrastructure.
AWS is a secure, durable technology platform with industry-recognized certifications and audits: PCI DSS Level 1, ISO 27001, FISMA Moderate, FedRAMP, HIPAA, and SOC 1 (formerly referred to as SAS 70 and/or SSAE 16) and SOC 2 audit reports. Our services and data centers have multiple layers of operational and physical security to ensure the integrity and safety of your data.
Other Products.
Amazon SageMaker Studio: Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. Accelerate innovation with purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows.
Amazon SageMaker Autopilot: Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models, and helps you automatically build, train, and tune the best ML model based on your data. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality.
Amazon SageMaker Ground Truth: Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows. These workflows support a variety of use cases including 3D point clouds, video, images, and text. As part of the workflows, labelers have access to assistive labeling features such as automatic 3D cuboid snapping, removal of distortion in 2D images, and auto-segment tools to reduce the time required to label datasets. In addition, Ground Truth offers automatic data labeling which uses a machine learning model to label your data.
Amazon SageMaker JumpStart: Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. Amazon SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models such as natural language processing, object detection, and image classification models.
Amazon SageMaker Data Wrangler: Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepares 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, cleaning, exploration, and visualization from a single visual interface. Using SageMaker Data Wrangler’s data selection tool, you can choose the data you want from various data sources and import it with a single click. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. With SageMaker Data Wrangler’s visualization templates, you can quickly preview and inspect that these transformations are completed as you intended by viewing them in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines and save them for reuse in the Amazon SageMaker Feature Store.
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Amazon SageMaker Debugger
As seen on DataEthics4All, Amazon SageMaker Debugger makes it easy to optimize machine learning (ML) models by capturing training metrics in real-time such as data loss during regression and sending alerts when anomalies are detected. This helps you immediately rectify
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SageMaker Debugger – Optimize ML models with real-time monitoring of training metrics and system resources
Amazon SageMaker Debugger makes it easy to optimize machine learning (ML) models by capturing training metrics in real-time such as data loss during regression and sending alerts when anomalies are detected. This helps you immediately rectify inaccurate model predictions such as an incorrect identification of an image. SageMaker Debugger automatically stops the training process when the desired accuracy is achieved, reducing the time and cost of training ML models.
User Reviews.
AI Society
About the Company.
In 2006, Amazon Web Services (AWS) began offering IT infrastructure services to businesses in the form of web services — now commonly known as cloud computing. One of the key benefits of cloud computing is the opportunity to replace up-front capital infrastructure expenses with low variable costs that scale with your business. With the Cloud, businesses no longer need to plan for and procure servers and other IT infrastructure weeks or months in advance. Instead, they can instantly spin up hundreds or thousands of servers in minutes and deliver results faster.
Today, Amazon Web Services provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world. With data center locations in the U.S., Europe, Brazil, Singapore, Japan, and Australia, customers across all industries are taking advantage of the following benefits:
AWS offers low, pay-as-you-go pricing with no up-front expenses or long-term commitments. They are able to build and manage a global infrastructure at scale and pass the cost-saving benefits onto you in the form of lower prices. With the efficiencies of our scale and expertise, they have been able to lower our prices on 15 different occasions over the past four years.
AWS provides a massive global cloud infrastructure that allows you to quickly innovate, experiment and iterate. Instead of waiting weeks or months for hardware, you can instantly deploy new applications, instantly scale up as your workload grows, and instantly scale down based on demand. Whether you need one virtual server or thousands, whether you need them for a few hours or 24/7, you still only pay for what you use.
AWS is a language and operating system agnostic platform. You choose the development platform or programming model that makes the most sense for your business. You can choose which services you use, one or several, and choose how you use them. This flexibility allows you to focus on innovation, not infrastructure.
AWS is a secure, durable technology platform with industry-recognized certifications and audits: PCI DSS Level 1, ISO 27001, FISMA Moderate, FedRAMP, HIPAA, and SOC 1 (formerly referred to as SAS 70 and/or SSAE 16) and SOC 2 audit reports. Our services and data centers have multiple layers of operational and physical security to ensure the integrity and safety of your data.
Other Products.
Amazon SageMaker Studio: Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. Accelerate innovation with purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows.
Amazon SageMaker Autopilot: Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models, and helps you automatically build, train, and tune the best ML model based on your data. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality.
Amazon SageMaker Ground Truth: Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows. These workflows support a variety of use cases including 3D point clouds, video, images, and text. As part of the workflows, labelers have access to assistive labeling features such as automatic 3D cuboid snapping, removal of distortion in 2D images, and auto-segment tools to reduce the time required to label datasets. In addition, Ground Truth offers automatic data labeling which uses a machine learning model to label your data.
Amazon SageMaker JumpStart: Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. Amazon SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models such as natural language processing, object detection, and image classification models.
Amazon SageMaker Data Wrangler: Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepares 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, cleaning, exploration, and visualization from a single visual interface. Using SageMaker Data Wrangler’s data selection tool, you can choose the data you want from various data sources and import it with a single click. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. With SageMaker Data Wrangler’s visualization templates, you can quickly preview and inspect that these transformations are completed as you intended by viewing them in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines and save them for reuse in the Amazon SageMaker Feature Store.
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Kore.ai SmartAssist
DataEthics4All introduces Kore.ai, which is a conversational AI and digital UX technology partner for Global 2000 companies. It provides conversational AI and digital UX-rich virtual assistants, designed specifically for enterprises, for a diverse range of use cases across industries for
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AI-first Customer Experience that automates voice and digital interactions with ease
Built on top of Kore.ai’s best in class, enterprise-grade ‘no-code’ platform, SmartAssist helps you transform your customer support for voice and digital channels with conversational AI and automation, improving customer experience and letting live agents focus on tasks where they are needed the most
User Reviews.
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About the Company.
Kore.ai is a market-leading conversational AI and digital UX technology partner for Global 2000 companies. It provides conversational AI and digital UX-rich virtual assistants, designed specifically for enterprises, for a diverse range of use cases across industries for engaging customers, employees, and partners. Its end-to-end, comprehensive Virtual Assistant Platform serves as a secure foundation for enterprises to design, build, test, host, and deploy AI-rich virtual assistants across 30+ different digital and voice channels. Kore.ai also offers specialized solutions focused on Enterprise Virtual Assistant, Retail Banking Services, HR, IT Helpdesk, and Customer Support services. Kore.ai partners with top ISVs and global system integrators for helping companies meet their digital transformation needs.
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Kore ‘No-code’ Platform: Blends Conversational AI and Digital UX
BankAssist: Conversational Banking
Employee Experience: IT Assist – 24×7 Instant IT support for routine issues
HR Assist – Manage routine HR queries with assistant
Agent Experience: AgentAssist – Provide customer intelligence for better service
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H2O.ai Hybrid Cloud
As seen on DataEthics4All, H2O AI Hybrid Cloud offers an end-to-end platform that democratizes artificial intelligence, enabling every employee, customer, and citizen with sophisticated AI technology and easy-to-use AI applications.
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Product Overview.
The H2O AI Hybrid Cloud Platform
The product helps organizations have access to world-class AI models with advanced automatic feature engineering for structured data, text, image, and video.
H2O AI Hybrid Cloud offers an end-to-end platform that democratizes artificial intelligence, enabling every employee, customer, and citizen with sophisticated AI technology and easy-to-use AI applications.
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About the Company.
H2O.ai is a software company based in Silicon Valley that created and reimagined what is possible. It is a company of creators that brought to market new platforms and technologies to drive the AI movement. They are the makers of, H2O, the leading open-source data science and machine learning platform trusted by over 18,000 organizations and hundreds of thousands of data scientists around the world.
H2O’s approach is to be open, transparent, and push the bleeding edge. Their philosophy is to create a culture of makers: community, customers, partners, entrepreneurs and our own “makers gonna make”. Their vision is to democratize AI for everyone. Not just a select few. The H2O Hybrid Cloud uses over 200 data connectors to clean any imbalanced data and transforms data into automatic data visualization.
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Sparkling Water: Gives access to H2O algorithms developed from the ground up for distributed computing and for both supervised and unsupervised approaches. Drives computation from Scala, R, or Python and use the H2O Flow UI, providing an ideal machine learning platform for application developers. Easy to deploy POJOs and MOJOs to deploy models for fast and accurate scoring in any environment, including very large models.
H2O Driverless AI: Allows automatic feature engineering, brings your own models to feature, used for image and natural language processing.
H2O Wave: Create interactive and visual AI applications with just Python. HTML, CSS, and Javascript skills that aren’t required.
Enterprise Puddle: The H2O.ai open source and the Driverless AI platforms allow data scientists to deploy models in any cloud. Deploy the machine learning model as a RESTful or serverless endpoint for real-time scoring directly in the cloud. Model building on H2O comes with limitless flexibility for the data teams. It is capable of end-to-end data science in your cloud – everything from feature engineering, machine learning, interpretability to deployment. Leverage the high availability, scalable storage, compute, and memory of public clouds to support your production workloads. Use H2O with GPUs and CPUs to train and score models in your cloud.
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ViSenze E-Commerce
DataEthics4All showcases ViSenze E-Commerce that lifts conversions and optimizes SEO by making it easier for shoppers to discover products with deep product attribute tagging.
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ViSenze – Discovery & Inspiration
ViSenze E-Commerce lifts conversions and optimizes SEO by making it easier for shoppers to discover products with deep product attribute tagging.
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AI Society
About the Company.
They help retailers to meet today’s shopper expectations and grow revenues, and they do so by transforming the customer journey and experience in a visual world with powerful intelligent search, personalization, and product recommendation solutions.
The prowess of their technology is born of and continues to be guided by their values.
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ViSenze In-Store: Enrich in-store experience by giving shoppers access to real-time stock availability and online ordering for out-of-stock items directly from your consumer app.
ViSenze Retail Operations: Influence your merchandising strategy and reduce wastage by accessing the most important visual data from the store to forecast sales performance of on-shelf display items based on past data.
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Did you know that Social Security data is used for much more than benefits computing?
Overview.
Social Security datasets are required by different categories of entities for various purposes, now more than ever. Social Security was originally intended to only provide benefits for retired, unemployed and disadvantaged Americans. However, by now, it covers more benefits, hence the universality of its use such as health insurance inclusion and its trust fund.
Slowly, the Social Security number became the single most adopted identification number in the United States of America both by government agencies and the private sector. The significance of this 9-digit number translates to the importance of the data connected to it that was harvested from the Social Security Administration (SSA) of the USA. It not only involves information about the beneficiaries of the social security but also about their respective employers and level of income and much more.
Besides computing benefit amounts for citizens eligible for the social insurance programs, the Social Security administrative data is relevant to private businesses, governmental as well as non-governmental organizations and also research labs where studies are conducted for policy evaluation, innovation and development. The datasets provided by the SSA fall into 3 different categories: Public, Restricted Public or Non-Public datasets.
Data Availability.
Despite the dynamic events that the United States of America has witnessed both on a national and global level in the previous 5 years, the Social Security Administration managed to provide a scaling number of datasets. The nature and proportion of these datasets, however, varies in time.
Figure 1: Social Security datasets’ percentages (2015-2020)
You may hover over the chart for more details.
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Looking at the Figure 1, a noticeable pattern shows the increase in Public datasets’ percentage from the total number datasets in the SSA from 2015 to 2020 from 41.01% to 71.51% and a less proportionate percentage decrease in both Non-Public and Restricted Public datasets that accounts to about half of the respective percentages from 2015 to 2020.
No matter the nature of the events that occurred throughout these 6 fiscal years, they make space for potential research studies on a limitless number of topics. R&D professionals tend to link Social Security data with their survey data to successfully conduct their studies. This is due to the restricted scope of variables in SSA’s public datasets.
On the other hand, restricted public and non-public datasets cover more ground in terms of features which makes them rare for the public. This is proven in the previous chart with the largest respective percentages of 50.73% and 8.26% from a total of 10,791 datasets in the year 2015.
The progressive change in these numbers poses a question regarding what happened in the US since 2016 that could have possibly stimulated this or been affected by it.
Data Use.
Ethical Data Use.
Figure 2: Number of SSA Public Datasets (2016-2020)
You may hover over the chart for more details and interactive options.
The chart illustrates an identical pattern in the number of public datasets from 2016 to 2020, specifically in the 4th quarter of every year. It also highlights a few alarming events for the American Citizen which could represent a probable reason for the observed pattern.
The SSA minimizes data politicization during presidential elections
SSA restricts over 100 datasets prior to Trump’s presidential election in 2016
One month prior to the presidential elections of Donald Trump in 2016, the number of publicly released datasets dropped from 969 to 811. This accounts to over 100 datasets that either have been restricted to a certain number of people or organizations or clasfified as non-public for the purpose of minimzing the risk of data exploitation and politicization for election polls or they were completely disposed of by the Social Security Administration.
SSA shows a decrease of 52 public datasets prior to Biden’s election in 2020
The drop in the number of public social security datasets also reoccured one month prior to the presidential elections of Joe Biden in 2020 and it accounted to a decrease of 52 datasets from September to November 2020. However, between the two previous elections, the loss gap has got slowly smaller than 100 over the years, especially after the national scandal of Cambridge Analytica Data Leaks in March 2018.
Documents from Cambridge Analytica in London revealed that the firm improperly obtained and used over 87 million Facebook user profiles in a transaction with Donald Trump’s presidential compaign where the scraped private Facebook data was used to build voter profiles and assist the candidate in the US presidential elections of 2016. This national data privacy crisis proved that data exploitation is not limited to commerical purposes and that it can target the American Citizen without their consent.
Social security data contributes in making the workplace safer
152 public datasets were restricted during the “Me Too” Movement in the US
The same phenomena occured with a drop rate of 13.18% in the beginning of the US “Me too” Movement from October to September 2017. “Me too” is a global movement that condemns sexual harrasement and gives a voice to its victims.
Social Security Administration reacts to data privacy
SSA gives access to 1312 public datasets on Data Privacy Day in January 2019
Crisis & Social Impact.
Figure 3: Social Security public datasets released in 2020
You may hover over the chart for more details.
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Social Security data becomes more available during Covid-19 crisis
SSA’s monthly public datasets reach their maximum of 1469 since the pandemic
Until the 2020 presidential elections, the number of public datasets released by the SSA in 2020 has been continuously increasing from 1430 to a maximum value of 1469 in order to satisfy these research needs. Moreover, non-governmental organizations were in need of social security data in the year 2020 as they worked on advocating equal health care opportunities such as First Covid-19 Vaccinations starting from December 2020 to all US social security beneficiaries which will probably result in a new rise in the beginning of 2021.
Social Security data contributes to #BLM Movement
SSA publicly releases 1457 datasets during #BLM movement
Protests for the Black Lives Matter globel movement that was initiated in the USA shortly after the death of George Floyd in May 2020 have generated a large number of petitions and fundraising campaigns regarding #BlackLivesMatter across the internet and numerous sensibilization events about ending racism globally. The movement also approached the BLM cause on an international level which perhaps pushed the SSA to move some datasets from being restricted or non-public to being publicly available to everyone everywhere.
This was proven in the fiscal year 2020, given the exceptionally high number of public datasets released of 1457 in May 2020. The data was used to highlight the gap between white and black people’s wages, benefits and much more with the purpose of raising awareness about the details of racism present in an american’s everyday activities.
TikTok App challenges data protection values of the SSA
SSA releases 1463 public datasets during TikTok US data harvest allegations
Review.
The Social Security Administration has been playing an active role in 2020 when it comes to endorsing public data to support Research & Development departments in different fields in the United States of America. This includes global health which is the world’s priority during the Covid-19 crisis and many more fields. The data that is publicly released on a yearly basis also contributes in recovery from other types of crisis, raising awareness about data privacy threats from social media or content creation giants and about data transparency when it comes to policy making and elections in the US. As for the restrictions made on private datasets to the administration, some are working on protecting US citizens data from political exploitation. Nonetheless, some are preventing NGOs from tackling certain topics and keeping the data from speaking for the voiceless. The SSA needs to open calls for datasets where every external organization can make a data usage proposal for certain restricted or non-public datasets added by the administration. How else would social security data be as transparent as the SSA claims? How else would the world use the data for positive social change and ethical purposes?
Data.
- Source: data.gov
- Publisher: Social Security Administration
- Retrieved: June 26th, 2021.
- Last Update: July 2nd, 2021. It’s monthly updated by the SSA to reflect new datasets (public, restricted, non-public)
- Description: Social Security continues to release data in support of the Open Data Initiative.
- URL: https://catalog.data.gov/dataset/enterprise-data-inventory-progress-information
Author.
Sarra Hannachi is a Master of Science Student in Business Analytics at Tunis Business School, Tunisia and a Data Storytelling Intern at DataEthics4All. She’s passionate about statistics and data science and about organizations that apply their data practices ethically and for the greater good. Sarra is also the leader of a Data Science Club “DSC TBS” in her campus for business students looking to enhance their skills in Data Science & AI and to enrich their knowledge of these fields.
Contact: LinkedIn
Ai-Media Live Captioning
As seen on DataEthics4All, Ai-Media make their leading captioning and translation technologies available to everyone around the world – across purposes, across industries, across platforms, and across languages.
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Product Overview.
Ai-Media Live Captioning
Ai-Media provides premium video accessibility services, integrations, and automated workflows, and tools & features that help make video accessibility easy.
Whether you broadcast on television or stream to your own YouTube channel, host lives events to a global audience, or deliver classes to a single student, their suite of live accessibility products ensures nobody misses out on what you are delivering.
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