Category: Learning Levels

In this post, we demonstrate one of the many options that you have to take advantage of AWS’s broadest and deepest set of AI/ML capabilities in a multicloud environment. We show how you can build and train an ML model in AWS and deploy the model in another platform. We train the model using Amazon SageMaker, store the model artifacts in Amazon Simple Storage Service (Amazon S3), and deploy and run the model in Azure.

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Although this enables parallelization of tasks across multiple nodes, leading to accelerated training times, enhanced scalability, and improved […]

Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. Often, LLMs need to interact with other software, databases, or APIs to accomplish complex tasks. […]

In first part of this multi-series blog post, you will learn how to create a scalable training pipeline and prepare training data for Comprehend Custom Classification models. We will introduce a custom classifier training pipeline that can be deployed in your AWS account with few clicks.

Today, generative AI models cover a variety of tasks from text summarization, Q&A, and image and video generation. To improve the quality of output, approaches like n-short learning, Prompt engineering, Retrieval Augmented Generation (RAG) and fine tuning are used. Fine-tuning allows you to adjust these generative AI models to achieve improved performance on your domain-specific […]

Searching for insights in a repository of free-form text documents can be like finding a needle in a haystack. A traditional approach might be to use word counting or other basic analysis to parse documents, but with the power of Amazon AI and machine learning (ML) tools, we can gather deeper understanding of the content. […]

Today, we are excited to announce the simplified Quick setup experience in Amazon SageMaker. With this new capability, individual users can launch Amazon SageMaker Studio with default presets in minutes. SageMaker Studio is an integrated development environment (IDE) for machine learning (ML). ML practitioners can perform all ML development steps—from preparing their data to building, […]

This post addresses the challenge faced by developers and support teams when application logs are presented in languages other than English, making it difficult for them to debug and provide support. The proposed solution uses Amazon Translate to automatically translate non-English logs in CloudWatch, and provides step-by-step guidance on deploying the solution in your environment.

In this post, we share how SageMaker facilitates the data science team at Scalable to manage the lifecycle of a data science project efficiently, namely the email classifier project. The lifecycle starts with the initial phase of data analysis and exploration with SageMaker Studio; moves on to model experimentation and deployment with SageMaker training, inference, and Hugging Face DLCs; and completes with a training pipeline with SageMaker Pipelines integrated with other AWS services

Amazon SageMaker Domain supports SageMaker machine learning (ML) environments, including SageMaker Studio and SageMaker Canvas. SageMaker Studio is a fully integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all ML development steps, from preparing data to building, training, and deploying your ML models, improving […]