AWS Machine Learning Blog
Category: HAQM SageMaker
How Cisco accelerated the use of generative AI with HAQM SageMaker Inference
This post highlights how Cisco implemented new functionalities and migrated existing workloads to HAQM SageMaker inference components for their industry-specific contact center use cases. By integrating generative AI, they can now analyze call transcripts to better understand customer pain points and improve agent productivity. Cisco has also implemented conversational AI experiences, including chatbots and virtual agents that can generate human-like responses, to automate personalized communications based on customer context. Additionally, they are using generative AI to extract key call drivers, optimize agent workflows, and gain deeper insights into customer sentiment. Cisco’s adoption of SageMaker Inference has enabled them to streamline their contact center operations and provide more satisfying, personalized interactions that address customer needs.
Automate the machine learning model approval process with HAQM SageMaker Model Registry and HAQM SageMaker Pipelines
This post illustrates how to use common architecture principles to transition from a manual monitoring process to one that is automated. You can use these principles and existing AWS services such as HAQM SageMaker Model Registry and HAQM SageMaker Pipelines to deliver innovative solutions to your customers while maintaining compliance for your ML workloads.
Inference AudioCraft MusicGen models using HAQM SageMaker
Music generation models have emerged as powerful tools that transform natural language text into musical compositions. Originating from advancements in artificial intelligence (AI) and deep learning, these models are designed to understand and translate descriptive text into coherent, aesthetically pleasing music. Their ability to democratize music production allows individuals without formal training to create high-quality […]
Monks boosts processing speed by four times for real-time diffusion AI image generation using HAQM SageMaker and AWS Inferentia2
This post is co-written with Benjamin Moody from Monks. Monks is the global, purely digital, unitary operating brand of S4Capital plc. With a legacy of innovation and specialized expertise, Monks combines an extraordinary range of global marketing and technology services to accelerate business possibilities and redefine how brands and businesses interact with the world. Its […]
Transition your HAQM Forecast usage to HAQM SageMaker Canvas
After careful consideration, we have made the decision to close new customer access to HAQM Forecast, effective July 29, 2024. HAQM Forecast existing customers can continue to use the service as normal. AWS continues to invest in security, availability, and performance improvements for HAQM Forecast, but we do not plan to introduce new features. HAQM […]
HAQM SageMaker inference launches faster auto scaling for generative AI models: up-to 6x faster scale-up detection
Today, we are excited to announce a new capability in HAQM SageMaker inference that can help you reduce the time it takes for your generative artificial intelligence (AI) models to scale automatically. This feature can detect the need for scaling model copies up-to 6x faster as compared to traditional mechanisms used by customers. You can […]
Evaluate conversational AI agents with HAQM Bedrock
As conversational artificial intelligence (AI) agents gain traction across industries, providing reliability and consistency is crucial for delivering seamless and trustworthy user experiences. However, the dynamic and conversational nature of these interactions makes traditional testing and evaluation methods challenging. Conversational AI agents also encompass multiple layers, from Retrieval Augmented Generation (RAG) to function-calling mechanisms that […]
LLM experimentation at scale using HAQM SageMaker Pipelines and MLflow
Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You may need to customize an LLM to adapt to your unique use case, improving its performance on your specific dataset or task. You can customize the model […]
Boosting Salesforce Einstein’s code generating model performance with HAQM SageMaker
This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. Salesforce, Inc. is an American cloud-based software company headquartered in San Francisco, California. It provides customer relationship management (CRM) software and applications focused on sales, customer service, marketing automation, […]
Use Llama 3.1 405B for synthetic data generation and distillation to fine-tune smaller models
Today, we are excited to announce the availability of the Llama 3.1 405B model on HAQM SageMaker JumpStart, and HAQM Bedrock in preview. The Llama 3.1 models are a collection of state-of-the-art pre-trained and instruct fine-tuned generative artificial intelligence (AI) models in 8B, 70B, and 405B sizes. HAQM SageMaker JumpStart is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. HAQM Bedrock offers a straightforward way to build and scale generative AI applications with Meta Llama models, using a single API.