Company Website

Google Cloud Text-to-Speech: Convert text into natural-sounding speech using an API powered by Google’s AI technologies. Train a custom voice model using your own audio recordings to create a unique and more natural-sounding voice for your organization. You can define and choose the voice profile that suits your organization and quickly adjust to changes in voice needs without needing to record new phrases.

Google Cloud Virtual Agents: Dialogflow CX allows enterprise customers to create advanced Virtual Agents in minutes that seamlessly switch between topics, handle supplemental questions, and operate across multiple channels 24/7 to minimize live agent interventions. Intuitive drag & drop visual flow builder makes it easy to build and maintain sophisticated conversations. Join over 1.5 million developers using Dialogflow.

Google Cloud Agent Assist: Empowers human agents with continuous support during their calls and chats by identifying intent and providing real-time, step-by-step assistance.

Google Cloud Natural Language: Derive insights from unstructured text using Google machine learning. Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.

Google Cloud Dialogflow CX: Lifelike conversational AI with state-of-the-art virtual agents. Available in two editions: Dialogflow CX (advanced), Dialogflow ES (standard). Reduce development time with interactive flow visualizations that allow builders to quickly see, understand, edit, and share their work. It also allows for easy collaboration across teams.

Company Website

Google Cloud Speech-to-Text: Accurately convert speech into text using an API powered by Google’s AI technologies. Customize speech recognition to transcribe domain-specific terms and rare words by providing hints and boost your transcription accuracy of specific words or phrases. Automatically convert spoken numbers into addresses, years, currencies, and more using classes.

Google Cloud Virtual Agents: Dialogflow CX allows enterprise customers to create advanced Virtual Agents in minutes that seamlessly switch between topics, handle supplemental questions, and operate across multiple channels 24/7 to minimize live agent interventions. Intuitive drag & drop visual flow builder makes it easy to build and maintain sophisticated conversations. Join over 1.5 million developers using Dialogflow.

Google Cloud Agent Assist: Empowers human agents with continuous support during their calls and chats by identifying intent and providing real-time, step-by-step assistance.

Google Cloud Natural Language: Derive insights from unstructured text using Google machine learning. Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.

Google Cloud Dialogflow CX: Lifelike conversational AI with state-of-the-art virtual agents. Available in two editions: Dialogflow CX (advanced), Dialogflow ES (standard). Reduce development time with interactive flow visualizations that allow builders to quickly see, understand, edit, and share their work. It also allows for easy collaboration across teams.

Company Website

Google Cloud Speech-to-Text: Accurately convert speech into text using an API powered by Google’s AI technologies. Customize speech recognition to transcribe domain-specific terms and rare words by providing hints and boost your transcription accuracy of specific words or phrases. Automatically convert spoken numbers into addresses, years, currencies, and more using classes.

Google Cloud Text-to-Speech: Convert text into natural-sounding speech using an API powered by Google’s AI technologies. Train a custom voice model using your own audio recordings to create a unique and more natural-sounding voice for your organization. You can define and choose the voice profile that suits your organization and quickly adjust to changes in voice needs without needing to record new phrases.

Google Cloud Agent Assist: Empowers human agents with continuous support during their calls and chats by identifying intent and providing real-time, step-by-step assistance.

Google Cloud Natural Language: Derive insights from unstructured text using Google machine learning. Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.

Google Cloud Dialogflow CX: Lifelike conversational AI with state-of-the-art virtual agents. Available in two editions: Dialogflow CX (advanced), Dialogflow ES (standard). Reduce development time with interactive flow visualizations that allow builders to quickly see, understand, edit, and share their work. It also allows for easy collaboration across teams.

Company Website

Google Cloud Speech-to-Text: Accurately convert speech into text using an API powered by Google’s AI technologies. Customize speech recognition to transcribe domain-specific terms and rare words by providing hints and boost your transcription accuracy of specific words or phrases. Automatically convert spoken numbers into addresses, years, currencies, and more using classes.

Google Cloud Text-to-Speech: Convert text into natural-sounding speech using an API powered by Google’s AI technologies. Train a custom voice model using your own audio recordings to create a unique and more natural-sounding voice for your organization. You can define and choose the voice profile that suits your organization and quickly adjust to changes in voice needs without needing to record new phrases.

Google Cloud Virtual Agents: Dialogflow CX allows enterprise customers to create advanced Virtual Agents in minutes that seamlessly switch between topics, handle supplemental questions, and operate across multiple channels 24/7 to minimize live agent interventions. Intuitive drag & drop visual flow builder makes it easy to build and maintain sophisticated conversations. Join over 1.5 million developers using Dialogflow.

Google Cloud Natural Language: Derive insights from unstructured text using Google machine learning. Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.

Google Cloud Dialogflow CX: Lifelike conversational AI with state-of-the-art virtual agents. Available in two editions: Dialogflow CX (advanced), Dialogflow ES (standard). Reduce development time with interactive flow visualizations that allow builders to quickly see, understand, edit, and share their work. It also allows for easy collaboration across teams.

Company Website

Google Cloud Speech-to-Text: Accurately convert speech into text using an API powered by Google’s AI technologies. Customize speech recognition to transcribe domain-specific terms and rare words by providing hints and boost your transcription accuracy of specific words or phrases. Automatically convert spoken numbers into addresses, years, currencies, and more using classes.

Google Cloud Text-to-Speech: Convert text into natural-sounding speech using an API powered by Google’s AI technologies. Train a custom voice model using your own audio recordings to create a unique and more natural-sounding voice for your organization. You can define and choose the voice profile that suits your organization and quickly adjust to changes in voice needs without needing to record new phrases.

Google Cloud Virtual Agents: Dialogflow CX allows enterprise customers to create advanced Virtual Agents in minutes that seamlessly switch between topics, handle supplemental questions, and operate across multiple channels 24/7 to minimize live agent interventions. Intuitive drag & drop visual flow builder makes it easy to build and maintain sophisticated conversations. Join over 1.5 million developers using Dialogflow.

Google Cloud Agent Assist: Empowers human agents with continuous support during their calls and chats by identifying intent and providing real-time, step-by-step assistance.

Google Cloud Dialogflow CX: Lifelike conversational AI with state-of-the-art virtual agents. Available in two editions: Dialogflow CX (advanced), Dialogflow ES (standard). Reduce development time with interactive flow visualizations that allow builders to quickly see, understand, edit, and share their work. It also allows for easy collaboration across teams.

Company Website

Google Cloud Speech-to-Text: Accurately convert speech into text using an API powered by Google’s AI technologies. Customize speech recognition to transcribe domain-specific terms and rare words by providing hints and boost your transcription accuracy of specific words or phrases. Automatically convert spoken numbers into addresses, years, currencies, and more using classes.

Google Cloud Text-to-Speech: Convert text into natural-sounding speech using an API powered by Google’s AI technologies. Train a custom voice model using your own audio recordings to create a unique and more natural-sounding voice for your organization. You can define and choose the voice profile that suits your organization and quickly adjust to changes in voice needs without needing to record new phrases.

Google Cloud Virtual Agents: Dialogflow CX allows enterprise customers to create advanced Virtual Agents in minutes that seamlessly switch between topics, handle supplemental questions, and operate across multiple channels 24/7 to minimize live agent interventions. Intuitive drag & drop visual flow builder makes it easy to build and maintain sophisticated conversations. Join over 1.5 million developers using Dialogflow.

Google Cloud Agent Assist: Empowers human agents with continuous support during their calls and chats by identifying intent and providing real-time, step-by-step assistance.

Google Cloud Natural Language: Derive insights from unstructured text using Google machine learning. Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.

Company Website

Google Cloud Speech-to-Text: Accurately convert speech into text using an API powered by Google’s AI technologies. Customize speech recognition to transcribe domain-specific terms and rare words by providing hints and boost your transcription accuracy of specific words or phrases. Automatically convert spoken numbers into addresses, years, currencies, and more using classes.

Google Cloud Text-to-Speech: Convert text into natural-sounding speech using an API powered by Google’s AI technologies. Train a custom voice model using your own audio recordings to create a unique and more natural-sounding voice for your organization. You can define and choose the voice profile that suits your organization and quickly adjust to changes in voice needs without needing to record new phrases.

Google Cloud Virtual Agents: Dialogflow CX allows enterprise customers to create advanced Virtual Agents in minutes that seamlessly switch between topics, handle supplemental questions, and operate across multiple channels 24/7 to minimize live agent interventions. Intuitive drag & drop visual flow builder makes it easy to build and maintain sophisticated conversations. Join over 1.5 million developers using Dialogflow.

Google Cloud Agent Assist: Empowers human agents with continuous support during their calls and chats by identifying intent and providing real-time, step-by-step assistance.

Google Cloud Natural Language: Derive insights from unstructured text using Google machine learning. Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.

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.

Company Website

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.

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.

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.

Company Website

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 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.

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.

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.

Company Website

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 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.