Cartesia Sonic 3 text-to-speech model is now available on Amazon SageMaker JumpStart Cartesia’s Sonic 3 model is now available in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. Sonic 3 is Cartesia's latest state space model (SSM) for streaming text-to-speech (TTS), delivering high naturalness, accurate transcript following, and industry-leading latency with fine-grained control over volume, speed, and emotion. Sonic 3 supports 42 languages and provides advanced controllability through API parameters and SSML tags for volume, speed, and emotion adjustments. The model includes natural laughter support, stable voices optimized for voice agents, and emotive voices for expressive characters. With sub-100ms latency, Sonic 3 enables real-time conversational AI that captures human speech nuances including emotions and tonal shifts. With SageMaker JumpStart, customers can deploy Sonic 3 with just a few clicks to address their voice AI use cases. To get started with this model, navigate to the SageMaker JumpStart model catalog in the SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html.

Cartesia Sonic 3 text-to-speech model is now available on Amazon SageMaker JumpStart

Cartesia’s Sonic 3 model is now available in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. Sonic ...

#AWS #AmazonSagemakerJumpstart #Aiml #AmazonSagemaker

Build Production-Ready Drug Discovery and Robotics Pipelines with NVIDIA NIMs on SageMaker JumpStart Amazon SageMaker JumpStart now enables one-click deployment of four NVIDIA NIMs models purpose-built for biosciences and physical AI: ProteinMPNN, Nemotron-3.5B-Instruct, MSA Search NIM, and Cosmos Reason. NVIDIA NIM™ provides prebuilt, optimized inference microservices for rapidly deploying the latest AI models on any NVIDIA-accelerated infrastructure. These models bring advanced capabilities spanning protein design, reasoning with configurable outputs, and physical world understanding, enabling customers to accelerate biosciences research, drug discovery, and embodied AI applications on AWS infrastructure. ProteinMPNN enables fast and efficient protein sequence optimization guided by structural data. This NIM generates high-quality sequences with enhanced binding affinity and stability, validated through experimental results. Designed for scalability and flexibility, ProteinMPNN integrates seamlessly into protein engineering workflows, transforming applications like enzyme design and therapeutic development. MSA Search NIM supports GPU-accelerated Multiple Sequence Alignment (MSA) of a query amino acid sequence against a set of protein sequence databases. These databases are searched for similar sequences to the query and then the collection of sequences are aligned to establish similar regions even when the proteins have different lengths and motifs. Nemotron-3.5B-Instruct delivers high reasoning performance, native tool calling support, and extended context processing with 256k token context window. This model employs an efficient hybrid Mixture-of-Experts (MoE) architecture to ensure higher throughput than its predecessors for agentic and coding workloads, while maintaining the reasoning depth of a larger model. It is ideal for building multi-agent workflows, developer productivity tools, processes automation, and for scientific and mathematical reasoning analysis, amongst others. Cosmos Reason is an open , customizable, reasoning vision language model (VLM) for physical AI and robotics. It enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding, and common sense to understand and act in the real world. This model understands space, time, and fundamental physics, and can serve as a planning model to reason what steps an embodied agent might take next. With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html.

Build Production-Ready Drug Discovery and Robotics Pipelines with NVIDIA NIMs on SageMaker JumpStart

Amazon SageMaker JumpStart now enables one-click deployment of four NVIDIA NIMs models purpose-built for biosciences and physical AI: Prot...

#AWS #AmazonSagemakerJumpstart #Aiml #AmazonSagemaker

Build Production-Ready Drug Discovery and Robotics Pipelines with NVIDIA NIMs on SageMaker JumpStart Amazon SageMaker JumpStart now enables one-click deployment of four NVIDIA NIMs models purpose-built for biosciences and physical AI: ProteinMPNN, Nemotron-3.5B-Instruct, MSA Search NIM, and Cosmos Reason. NVIDIA NIM™ provides prebuilt, optimized inference microservices for rapidly deploying the latest AI models on any NVIDIA-accelerated infrastructure. These models bring advanced capabilities spanning protein design, reasoning with configurable outputs, and physical world understanding, enabling customers to accelerate biosciences research, drug discovery, and embodied AI applications on AWS infrastructure. ProteinMPNN enables fast and efficient protein sequence optimization guided by structural data. This NIM generates high-quality sequences with enhanced binding affinity and stability, validated through experimental results. Designed for scalability and flexibility, ProteinMPNN integrates seamlessly into protein engineering workflows, transforming applications like enzyme design and therapeutic development. MSA Search NIM supports GPU-accelerated Multiple Sequence Alignment (MSA) of a query amino acid sequence against a set of protein sequence databases. These databases are searched for similar sequences to the query and then the collection of sequences are aligned to establish similar regions even when the proteins have different lengths and motifs. Nemotron-3.5B-Instruct delivers high reasoning performance, native tool calling support, and extended context processing with 256k token context window. This model employs an efficient hybrid Mixture-of-Experts (MoE) architecture to ensure higher throughput than its predecessors for agentic and coding workloads, while maintaining the reasoning depth of a larger model. It is ideal for building multi-agent workflows, developer productivity tools, processes automation, and for scientific and mathematical reasoning analysis, amongst others. Cosmos Reason is an open , customizable, reasoning vision language model (VLM) for physical AI and robotics. It enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding, and common sense to understand and act in the real world. This model understands space, time, and fundamental physics, and can serve as a planning model to reason what steps an embodied agent might take next. With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

🆕 Amazon SageMaker JumpStart provides four NVIDIA NIMs models for biosciences and AI: ProteinMPNN, Nemotron-3.5B-Instruct, MSA Search NIM, and Cosmos Reason, for quick AI deployment in drug discovery, protein design, and robotics on AWS with o…

#AWS #AmazonSagemakerJumpstart #Aiml #AmazonSagemaker

DeepSeek OCR, MiniMax M2.1, and Qwen3-VL-8B-Instruct models are now available on SageMaker JumpStart Today, AWS announced the availability of DeepSeek OCR, MiniMax M2.1, and Qwen3-VL-8B-Instruct in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning document intelligence, multilingual coding, advanced multimodal reasoning, and vision-language understanding, enabling customers to build sophisticated AI applications across diverse use cases on AWS infrastructure. These models address different enterprise AI challenges with specialized capabilities: DeepSeek OCR explores visual-text compression for document processing. It can extract structured information from forms, invoices, diagrams, and complex documents with dense text layouts. MiniMax M2.1 is optimized for coding, tool use, instruction following, and long-horizon planning. It automates multilingual software development and executes complex, multi-step office workflows, empowering developers to build autonomous applications. Qwen3-VL-8B-Instruct delivers ssuperior text understanding and generation, deeper visual perception and reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html. 

DeepSeek OCR, MiniMax M2.1, and Qwen3-VL-8B-Instruct models are now available on SageMaker JumpStart

Today, AWS announced the availability of DeepSeek OCR, MiniMax M2.1, and Qwen3-VL-8B-Instruct in Amazon SageMaker JumpStart, expanding the portf...

#AWS #AmazonSagemaker #AmazonSagemakerJumpstart

DeepSeek OCR, MiniMax M2.1, and Qwen3-VL-8B-Instruct models are now available on SageMaker JumpStart Today, AWS announced the availability of DeepSeek OCR, MiniMax M2.1, and Qwen3-VL-8B-Instruct in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning document intelligence, multilingual coding, advanced multimodal reasoning, and vision-language understanding, enabling customers to build sophisticated AI applications across diverse use cases on AWS infrastructure. These models address different enterprise AI challenges with specialized capabilities: DeepSeek OCR explores visual-text compression for document processing. It can extract structured information from forms, invoices, diagrams, and complex documents with dense text layouts. MiniMax M2.1 is optimized for coding, tool use, instruction following, and long-horizon planning. It automates multilingual software development and executes complex, multi-step office workflows, empowering developers to build autonomous applications. Qwen3-VL-8B-Instruct delivers ssuperior text understanding and generation, deeper visual perception and reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

🆕 AWS now offers DeepSeek OCR, MiniMax M2.1, and Qwen3-VL-8B-Instruct on SageMaker JumpStart, enhancing foundation models for document intelligence, coding, and multimodal reasoning. Deploy with clicks to tackle diverse AI challenges.

#AWS #AmazonSagemaker #AmazonSagemakerJumpstart

MiniMax-M2 is now available on Amazon SageMaker JumpStart MiniMax-M2 is now available on Amazon SageMaker JumpStart, providing customers with immediate access to deploy this efficient open-source model in minutes. With SageMaker JumpStart, you can quickly discover, evaluate, and deploy MiniMax-M2 using either SageMaker Studio's intuitive interface or the SageMaker Python SDK for programmatic deployment. MiniMax-M2 redefines efficiency for agents. It's a compact, fast, and cost-effective MoE model (230 billion total parameters with 10 billion active parameters) built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. To learn more about deploying foundation models with SageMaker JumpStart, deployment options with the SDK, and best practices for implementation, refer to ourhttps://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html MiniMax-M2 is available in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Jakarta), Canada (Central), Europe (Frankfurt), Europe (Stockholm), Europe (Ireland), Europe (London), Europe (Paris), South America (São Paulo).

MiniMax-M2 is now available on Amazon SageMaker JumpStart

MiniMax-M2 is now available on Amazon SageMaker JumpStart, providing customers with immediate access to deploy this efficient open-source model in minutes. With SageMaker JumpStart, you can quickly discov...

#AWS #AmazonSagemakerJumpstart

MiniMax-M2 is now available on Amazon SageMaker JumpStart MiniMax-M2 is now available on Amazon SageMaker JumpStart, providing customers with immediate access to deploy this efficient open-source model in minutes. With SageMaker JumpStart, you can quickly discover, evaluate, and deploy MiniMax-M2 using either SageMaker Studio's intuitive interface or the SageMaker Python SDK for programmatic deployment. MiniMax-M2 redefines efficiency for agents. It's a compact, fast, and cost-effective MoE model (230 billion total parameters with 10 billion active parameters) built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. To learn more about deploying foundation models with SageMaker JumpStart, deployment options with the SDK, and best practices for implementation, refer to our documentation. MiniMax-M2 is available in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Jakarta), Canada (Central), Europe (Frankfurt), Europe (Stockholm), Europe (Ireland), Europe (London), Europe (Paris), South America (São Paulo).

🆕 Amazon SageMaker JumpStart now offers MiniMax-M2, an efficient open-source model for quick deployment in coding and agentic tasks. Available globally, it's compact, fast, and cost-effective with 230B parameters. Use SageMaker Studio or SDK for deployment.

#AWS #AmazonSagemakerJumpstart

OpenAI open weight models now available on AWS AWS continues to expand access to the most advanced foundation models with OpenAI open weight models now available in Amazon Bedrock and Amazon SageMaker JumpStart. Accessing these new models from OpenAI on AWS, gpt-oss-120b and gpt-oss-20b, gives you more freedom to innovate and choose the right model for your specific use cases while maintaining complete control over your infrastructure and data.

OpenAI open weight models now available on AWS

AWS continues to expand access to the most advanced foundation models with OpenAI ope...

#AWS #AmazonBedrock #AmazonMachineLearning #AmazonSagemaker #AmazonSagemakerJumpstart #Announcements #ArtificialIntelligence #Featured #Launch #News #OpenSource

OpenAI open weight models now available on AWS AWS continues to expand access to the most advanced foundation models with OpenAI open weight models now available in Amazon Bedrock and Amazon SageMaker JumpStart. Accessing these new models from OpenAI on AWS, gpt-oss-120b and gpt-oss-20b, gives you more freedom to innovate and choose the right model for your specific use cases while maintaining complete control over your infrastructure and data.

OpenAI open weight models now available on AWS

AWS continues to expand access to the most advanced foundation models with OpenAI open weight m...

#AWS #AmazonBedrock #AmazonMachineLearning #AmazonSagemaker #AmazonSagemakerJumpstart #Announcements #ArtificialIntelligence #Launch #News #OpenSource

Meta’s Llama 4 now available in Amazon SageMaker JumpStart The first models in the new Llama 4 herd of models—Llama 4 Scout 17B and Llama 4 Maverick 17B—are now available on AWS. You can access Llama 4 models in Amazon SageMaker JumpStart. These advanced multimodal models empower you to build more tailored applications that respond to multiple types of media. Llama 4 offers improved performance at lower cost compared to Llama 3, with expanded language support for global applications. Featuring mixture-of-experts (MoE) architecture, these models deliver efficient multimodal processing for text and image inputs, improved compute efficiency, and enhanced AI safety measures. According to Meta, the smaller Llama 4 Scout 17B model, is the best multimodal model in the world in its class, and is more powerful than Meta’s Llama 3 models. Scout is a general-purpose model with 17 billion active parameters, 16 experts, and 109 billion total parameters that delivers state-of-the-art performance for its class. Scout significantly increases the context length from 128K in Llama 3, to an industry leading 10 million tokens. This opens up a world of possibilities, including multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast code bases. Llama 4 Maverick 17B is a general-purpose model that comes in both quantized (FP8) and non-quantized (BF16) versions, featuring 128 experts, 400 billion total parameters, and a 1 million context length. It excels in image and text understanding across 12 languages, making it suitable for versatile assistant and chat applications. Meta’s Llama 4 models are available in https://aws.amazon.com/sagemaker-ai/jumpstart/ in the US East (N. Virginia) AWS Region. To learn more, read the https://www.aboutamazon.com/news/aws/aws-meta-llama-4-models-available and https://aws.amazon.com/blogs/machine-learning/llama-4-family-of-models-from-meta-are-now-available-in-sagemaker-jumpstart/. These models can be accessed in the https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-launch.html.

Meta’s Llama 4 now available in Amazon SageMaker JumpStart

The first models in the new Llama 4 herd of models—Llama 4 Scout 17B and Llama 4 Maverick 17B—are now available on AWS. You can access Llama 4 models in Amazon SageMaker JumpStart....

#AWS #AmazonSagemaker #AmazonSagemakerJumpstart

Meta’s Llama 4 now available in Amazon SageMaker JumpStart The first models in the new Llama 4 herd of models—Llama 4 Scout 17B and Llama 4 Maverick 17B—are now available on AWS. You can access Llama 4 models in Amazon SageMaker JumpStart. These advanced multimodal models empower you to build more tailored applications that respond to multiple types of media. Llama 4 offers improved performance at lower cost compared to Llama 3, with expanded language support for global applications. Featuring mixture-of-experts (MoE) architecture, these models deliver efficient multimodal processing for text and image inputs, improved compute efficiency, and enhanced AI safety measures. According to Meta, the smaller Llama 4 Scout 17B model, is the best multimodal model in the world in its class, and is more powerful than Meta’s Llama 3 models. Scout is a general-purpose model with 17 billion active parameters, 16 experts, and 109 billion total parameters that delivers state-of-the-art performance for its class. Scout significantly increases the context length from 128K in Llama 3, to an industry leading 10 million tokens. This opens up a world of possibilities, including multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast code bases. Llama 4 Maverick 17B is a general-purpose model that comes in both quantized (FP8) and non-quantized (BF16) versions, featuring 128 experts, 400 billion total parameters, and a 1 million context length. It excels in image and text understanding across 12 languages, making it suitable for versatile assistant and chat applications. Meta’s Llama 4 models are available in Amazon SageMaker JumpStart in the US East (N. Virginia) AWS Region. To learn more, read the launch blog and technical blog. These models can be accessed in the Amazon SageMaker Studio.

🆕 Meta's new Llama 4 models, Scout 17B and Maverick 17B, are on Amazon SageMaker JumpStart. They offer better performance, safety, and language support at lower cost, available in US East (N. Virginia).

#AWS #AmazonSagemaker #AmazonSagemakerJumpstart

AWS Weekly Roundup: AWS Developer Day, Trust Center, Well-Architected for Enterprises, and more (Feb 17, 2025) Join us for the AWS Developer Day on February 20! This virtual event is designed to help developers and teams incorporate cutting-edge yet responsible generative AI across their development lifecycle to accelerate innovation. In his keynote, Jeff Barr, Vice President of AWS Evangelism, shares his thoughts on the next generation of software development based on […]

AWS Weekly Roundup: AWS Developer Day, Trust Center, Well-Architected for Enterprises, and more (Feb 17, 2025)

Join us for the AWS Developer Day on ...

#AWS #AmazonQDeveloper #AmazonSagemakerJumpstart #Announcements #AwsAmplify #AwsVerifiedAccess #ElasticLoadBalancing #Launch #News #WeekInReview

Happy New Year! AWS Weekly Roundup: 2025 Tech Predictions, Llama 3.3 70B, Stable Diffusion 3.5 Large, custom billing view, and more (January 6, 2025) Happy New Year! We are witnessing technology augment human ingenuity in inspiring ways. In the coming years, using technology for positive impact will redefine the way we think about success. Amazon CTO, Dr. Werner Vogels, offers five forward-looking tech predictions for 2025, and beyond: The workforce of tomorrow is mission-driven A new era of energy […]

Happy New Year! AWS Weekly Roundup: 2025 Tech Predictions, Llama 3.3 70B, Stable Diffusion 3.5 Large, custom billing view, and more (January 6, 2025)

Happy New Year! We are witn...

#AWS #AmazonBedrock #AmazonSagemakerJumpstart #Announcements #Billing&AccountManagement #Launch #News #WeekInReview

Happy New Year! AWS Weekly Roundup: 2025 Tech Predictions, Llama 3.3 70B, Stable Diffusion 3.5 Large, custom billing view, and more (January 6, 2025) Happy New Year! We are witnessing technology augment human ingenuity in inspiring ways. In the coming years, using technology for positive impact will redefine the way we think about success. Amazon CTO, Dr. Werner Vogels, offers five forward-looking tech predictions for 2025, and beyond: The workforce of tomorrow is mission-driven A new era of energy […]

Happy New Year! AWS Weekly Roundup: 2025 Tech Predictions, Llama 3.3 70B, Stable Diffusion 3.5 Large, custom billing view, and more (January 6, 2025)

Happy New Year! We are witn...

#AWS #AmazonBedrock #AmazonSagemakerJumpstart #Announcements #Billing&AccountManagement #Launch #News #WeekInReview

Llama 3.3 70B now available on AWS via Amazon SageMaker JumpStart AWS customers can now access the Llama 3.3 70B model from Meta through Amazon SageMaker JumpStart. The Llama 3.3 70B model balances high performance with computational efficiency. It also delivers output quality comparable to larger Llama versions while requiring significantly fewer resources, making it an excellent choice for cost-effective AI deployments. Llama 3.3 70B features an enhanced attention mechanism that substantially reduces inference costs. Trained on approximately 15 trillion tokens, including web-sourced content and synthetic examples, the model underwent extensive supervised fine-tuning and Reinforcement Learning from Human Feedback (RLHF). This approach aligns outputs more closely with human preferences while maintaining high performance standards. According to Meta, this efficiency gain translates to nearly five times more cost-effective inference operations, making it an attractive option for production deployments. Customers can deploy Llama 3.3 70B through the SageMaker JumpStart user interface or programmatically using the SageMaker Python SDK. SageMaker AI's advanced inference capabilities help optimize both performance and cost efficiency for your deployments, allowing you to take full advantage of Llama 3.3 70B's inherent efficiency while benefiting from a streamlined deployment process. The Llama 3.3 70B model is available in all AWS Regions where Amazon SageMaker AI is available. To learn more about deploying Llama 3.3 70B on Amazon SageMaker JumpStart, see the documentation or https://aws.amazon.com/blogs/machine-learning/llama-3-3-70b-now-available-in-amazon-sagemaker-jumpstart/  

Llama 3.3 70B now available on AWS via Amazon SageMaker JumpStart

AWS customers can now access the Llama 3.3 70B model from Meta through Amazon SageMaker JumpStart. The Llama 3.3 70B model balances high performance with computational efficiency. It also delivers...

#AWS #AmazonSagemakerJumpstart

Llama 3.3 70B now available on AWS via Amazon SageMaker JumpStart AWS customers can now access the Llama 3.3 70B model from Meta through Amazon SageMaker JumpStart. The Llama 3.3 70B model balances high performance with computational efficiency. It also delivers output quality comparable to larger Llama versions while requiring significantly fewer resources, making it an excellent choice for cost-effective AI deployments. Llama 3.3 70B features an enhanced attention mechanism that substantially reduces inference costs. Trained on approximately 15 trillion tokens, including web-sourced content and synthetic examples, the model underwent extensive supervised fine-tuning and Reinforcement Learning from Human Feedback (RLHF). This approach aligns outputs more closely with human preferences while maintaining high performance standards. According to Meta, this efficiency gain translates to nearly five times more cost-effective inference operations, making it an attractive option for production deployments. Customers can deploy Llama 3.3 70B through the SageMaker JumpStart user interface or programmatically using the SageMaker Python SDK. SageMaker AI's advanced inference capabilities help optimize both performance and cost efficiency for your deployments, allowing you to take full advantage of Llama 3.3 70B's inherent efficiency while benefiting from a streamlined deployment process. The Llama 3.3 70B model is available in all AWS Regions where Amazon SageMaker AI is available. To learn more about deploying Llama 3.3 70B on Amazon SageMaker JumpStart, see the documentation or read the blog.

🆕 Llama 3.3 70B now available on AWS via Amazon SageMaker JumpStart

#AWS #AmazonSagemakerJumpstart

AWS Weekly Roundup: AWS BuilderCards at re:Invent 2024, AWS Community Day, Amazon Bedrock, vector databases, and more (Nov 18, 2024) This week, we wrapped up the final 2024 Latin America Amazon Web Services (AWS) Community Days of the year in Brazil, with multiple parallel events taking place. In Goiânia, we had Marcelo Palladino, senior developer advocate, and Marcelo Paiva, AWS Community Builder, as keynote speakers. Florianópolis feature Ana Cunha, senior developer advocate, and in Santiago […]

AWS Weekly Roundup: AWS BuilderCards at re:Invent 2024, AWS Community Day, Amazon Bedrock, vector databases, and more (Nov 18, 2024)

Th...

#AWS #AmazonBedrock #AmazonSagemakerJumpstart #AmazonTranscribe #AmazonTranslate #Announcements #ArtificialIntelligence #AwsOrganizations #News #WeekInReview