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Discover how @Amazon Web Services (AWS) lets you leverage ML models with Amazon SageMaker! ✨ Visit this link to try a tutorial or learn how you can get started. 💡With SageMaker, you can access, label, and process large amounts of both structured and unstructured data for ML If you have questions, The Amaral Group, LLC is here to help.

Discover how @Amazon Web Services (AWS) lets you leverage ML models with Amazon SageMaker! ✨ Visit this link to try a tutorial or learn how you can get started. 💡With SageMaker, you can access, label, and process large amounts of both structured and unstructured data for ML If you have questions, IT-HenHouse LLC is here to help.

Discover how @Amazon Web Services (AWS) lets you leverage ML models with Amazon SageMaker! ✨ Visit this link to try a tutorial or learn how you can get started. 💡With SageMaker, you can access, label, and process large amounts of both structured and unstructured data for ML If you have questions, TEKPROS INC is here to help.

Discover how @Amazon Web Services (AWS) lets you leverage ML models with Amazon SageMaker! ✨ Visit this link to try a tutorial or learn how you can get started. 💡With SageMaker, you can access, label, and process large amounts of both structured and unstructured data for ML If you have questions, Rack Systems is here to help.

Discover how @Amazon Web Services (AWS) lets you leverage ML models with Amazon SageMaker! ✨ Visit this link to try a tutorial or learn how you can get started. 💡With SageMaker, you can access, label, and process large amounts of both structured and unstructured data for ML If you have questions, Hypertec Solutions Partner is here to help.

Cohere’s compression-aware model training techniques allows the model to output embeddings in binary and int8 precision format, which are significantly smaller in size than the often used FP32 precision format, with minimal accuracy degradation. This unlocks the ability to run your enterprise search applications faster, cheaper, and more efficiently. Amazon Bedrock now supports compressed embeddings from Cohere Embed #aws #genAI #embeddingmodels #cohere #bedrock

Build accurate and efficient financial search applications using the Cohere multilingual embedding foundation model in Amazon Bedrock. 🔧☁️🤖 https://go.aws/3tZ0H4D In this blog, you'll learn how Cohere's embedding model sorts through noisy data inputs, adapts to complex RAG systems, and delivers cost-efficiency from its compression-aware training method, while boosting the productivity and output quality of financial analysts. #AWS #AmazonBedrock #generativeAI

Companies of all sizes and across every industry are looking to build or choose AI services for their business needs (call center operations, personalized recommendations, fraud detection, content analysis and moderation, and so much more.) Regardless of where you are in this journey, now would be a great time to assess and apply AI/ML architectural best practices across your enterprise. #machinelearning Have you looked at AWS's Well Architected Framework Review (#WAFR) - AI/ML best practices? https://lnkd.in/evBA3BZV AWS (#AWS) AI/ML best practices are organized across 6 best practices pillars, They help you understand the benefits and risks of decisions while building AI/ML workloads. Quick examples of best practices and recommendations are: - How do you define return on your investment and total cost of ownership for ML Workloads? - Do you have established ML roles and responsibilities in your organizations?  - How do you define the priorities of your AI/ML workloads? - How secure are your ML environments? Do you use pre-trained models or managed AI services?  - How do you test and deploy ML workloads? - What are your Key Performance Indicators (KPIs), when it comes to ML workloads and ML models ?  - Does using pretrained models and managed AWS AI services help you reduce cost and your carbon footprint? AWS's AI/ML best practices are available in public documentation, and can also be leveraged through the AWS Well Architected service in your AWS consoles. (And additionally, you now have Amazon Q to dive deep into any of these best practices! #amazonq ) Reach out to your AWS contacts for any additional questions or help with incorporating these best practices! 

Cohere Compressed embeddings can save you 32x on your vector DB cost, while keeping 100% search quality. Now also available on AWS Bedrock.

🌟 Cohere Embed v3, supporting int8 and binary embeddings - outperforms competitors in 18 languages (nDCG@10) while slashing memory costs by 100x—from $130k to $1,300 annually for 250 million embeddings 💫 Amazon Web Services (AWS) #AI #SemanticSearch #EmbeddingTechnology #MachineLearning