Amazon SageMaker has redesigned its Python SDK to provide a unified object-oriented interface that makes it straightforward to interact with SageMaker services. The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK (SageMaker Core) provides access to full breadth of SageMaker features andContinue Reading
In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and its benefits, and showed you how to fine-tune a Meta Llama 3.1 8B model on a custom dataset. In this post, we look at the enhancements to the ModelBuilder class,Continue Reading
In Part 1 of this series, we introduced Amazon SageMaker Fast Model Loader, a new capability in Amazon SageMaker that significantly reduces the time required to deploy and scale large language models (LLMs) for inference. We discussed how this innovation addresses one of the major bottlenecks in LLM deployment: the timeContinue Reading
The generative AI landscape has been rapidly evolving, with large language models (LLMs) at the forefront of this transformation. These models have grown exponentially in size and complexity, with some now containing hundreds of billions of parameters and requiring hundreds of gigabytes of memory. As LLMs continue to expand, AIContinue Reading
According to the National Cancer Institute, a cancer biomarker is a “biological molecule found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or of a condition or disease such as cancer.” Biomarkers typically differentiate an affected patient from a person withoutContinue Reading
The financial and banking industry can significantly enhance investment research by integrating generative AI into daily tasks like financial statement analysis. By taking advantage of advanced natural language processing (NLP) capabilities and data analysis techniques, you can streamline common tasks like these in the financial industry: Automating data extraction –Continue Reading
Amazon SageMaker Ground Truth enables the creation of high-quality, large-scale training datasets, essential for fine-tuning across a wide range of applications, including large language models (LLMs) and generative AI. By integrating human annotators with machine learning, SageMaker Ground Truth significantly reduces the cost and time required for data labeling. WhetherContinue Reading
Conducting assessments on application portfolios that need to be migrated to the cloud can be a lengthy endeavor. Despite the existence of AWS Application Discovery Service or the presence of some form of configuration management database (CMDB), customers still face many challenges. These include time taken for follow-up discussions withContinue Reading
Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. Data scientists face numerous challenges throughout this process, such as selecting appropriate tools, needing step-by-step instructions with code samples, and troubleshooting errors and issues. These iterative challenges can hinderContinue Reading
In recent years, FM sizes have been increasing. It is important to consider the massive amount of compute often required to train these models. The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia, customContinue Reading
