Category: HAQM SageMaker

Fine-tuning Meta Llama 3.1 models with HAQM SageMaker JumpStart enables developers to customize these publicly available foundation models (FMs). The Meta Llama 3.1 collection represents a significant advancement in the field of generative artificial intelligence (AI), offering a range of capabilities to create innovative applications. The Meta Llama 3.1 models come in various sizes, with 8 billion, 70 billion, and 405 billion parameters, catering to diverse project needs. In this post, we demonstrate how to fine-tune Meta Llama 3-1 pre-trained text generation models using SageMaker JumpStart.

HAQM DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. HAQM DataZone allows you to create and manage data zones, which are virtual data lakes that store and process your data, without the need for extensive coding or […]

This post demonstrates how you can bring your existing SageMaker Data Wrangler flows—the instructions created when building data transformations—from SageMaker Studio Classic to SageMaker Canvas. We provide an example of moving files from SageMaker Studio Classic to HAQM Simple Storage Service (HAQM S3) as an intermediate step before importing them into SageMaker Canvas.

While presigned URLs offer a convenient way to grant temporary access to S3 objects, sharing these URLs with people outside of the workteam can lead to unintended access of those objects. To mitigate this risk and enhance the security of SageMaker Ground Truth labeling tasks, we have introduced a new feature that adds an additional layer of security by restricting access to the presigned URLs to the worker’s IP address or virtual private cloud (VPC) endpoint from which they access the labeling task. In this blog post, we show you how to enable this feature, allowing you to enhance your data security as needed, and outline the success criteria for this feature, including the scenarios where it will be most beneficial.

The Cohere Rerank 3 Nimble foundation model (FM) is now generally available in HAQM SageMaker JumpStart. This model is the newest FM in Cohere’s Rerank model series, built to enhance enterprise search and Retrieval Augmented Generation (RAG) systems. In this post, we discuss the benefits and capabilities of this new model with some examples. Overview […]

HAQM SageMaker Canvas now empowers enterprises to harness the full potential of their data by enabling support of petabyte-scale datasets. Starting today, you can interactively prepare large datasets, create end-to-end data flows, and invoke automated machine learning (AutoML) experiments on petabytes of data—a substantial leap from the previous 5 GB limit. With over 50 connectors, […]

For organizations looking beyond the use of out-of-the-box Splunk AI/ML features, this post explores how HAQM SageMaker Canvas, a no-code ML development service, can be used in conjunction with data collected in Splunk to drive actionable insights. We also demonstrate how to use the generative AI capabilities of SageMaker Canvas to speed up your data exploration and help you build better ML models.

Webex by Cisco is a leading provider of cloud-based collaboration solutions which includes video meetings, calling, messaging, events, polling, asynchronous video and customer experience solutions like contact center and purpose-built collaboration devices. Webex’s focus on delivering inclusive collaboration experiences fuels our innovation, which leverages AI and Machine Learning, to remove the barriers of geography, language, personality, and familiarity with technology. Its solutions are underpinned with security and privacy by design. Webex works with the world’s leading business and productivity apps – including AWS. This blog post highlights how Cisco implemented faster autoscaling release reference.

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.

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.