Category: HAQM SageMaker

In this post, we show how to create an automated continuous integration and delivery (CI/CD) pipeline solution to build, scan, and deploy custom Docker images to SageMaker Studio domains. You can use this solution to promote consistency of the analytical environments for data science teams across your enterprise.

In this post, we show you how HAQM Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting. We dive into HAQM SageMaker Canvas and explain how SageMaker Canvas can solve forecasting challenges for retail and consumer packaged goods (CPG) enterprises.

HAQM Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and HAQM through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. In this post, I’ll show you how to use HAQM Bedrock—with its fully managed, on-demand API—with your HAQM SageMaker trained or fine-tuned model.

In this post, we walk you through the process to build an automated mechanism using HAQM SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the HAQM SageMaker Model Registry for your customers to use it.

In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. We use HuggingFace’s Optimum-Neuron software development kit (SDK) to apply LoRA to fine-tuning jobs, and use SageMaker HyperPod as the primary compute cluster to perform distributed training on Trainium. Using LoRA supervised fine-tuning for Meta Llama 3 models, you can further reduce your cost to fine tune models by up to 50% and reduce the training time by 70%.

In this blog post, we provide an introduction to preparing your own dataset for LLM training. Whether your goal is to fine-tune a pre-trained model for a specific task or to continue pre-training for domain-specific applications, having a well-curated dataset is crucial for achieving optimal performance.

Fastweb, one of Italy’s leading telecommunications operators, recognized the immense potential of AI technologies early on and began investing in this area in 2019. In this post, we explore how Fastweb used cutting-edge AI and ML services to embark on their LLM journey, overcoming challenges and unlocking new opportunities along the way.

TUI Group is one of the world’s leading global tourism services, providing 21 million customers with an unmatched holiday experience in 180 regions. The TUI content teams are tasked with producing high-quality content for its websites, including product details, hotel information, and travel guides, often using descriptions written by hotel and third-party partners. In this post, we discuss how we used HAQM SageMaker and HAQM Bedrock to build a content generator that rewrites marketing content following specific brand and style guidelines.

Today, we are excited to announce that the Llama 3.3 70B from Meta is available in HAQM SageMaker JumpStart. Llama 3.3 70B marks an exciting advancement in large language model (LLM) development, offering comparable performance to larger Llama versions with fewer computational resources. In this post, we explore how to deploy this model efficiently on HAQM SageMaker AI, using advanced SageMaker AI features for optimal performance and cost management.

Ensemble models are becoming popular within the ML communities. They generate more accurate predictions through combining the predictions of multiple models. Pipelines can quickly be used to create and end-to-end ML pipeline for ensemble models. This enables developers to build highly accurate models while maintaining efficiency, and reproducibility. In this post, we provide an example of an ensemble model that was trained and deployed using Pipelines.