Category: Amazon SageMaker

Amazon SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate machine learning (ML) predictions for their business needs. Starting today, SageMaker Canvas supports advanced model build configurations such as selecting a training method (ensemble or hyperparameter optimization) and algorithms, customizing the training and validation data split ratio, and […]

Building foundation models (FMs) requires building, maintaining, and optimizing large clusters to train models with tens to hundreds of billions of parameters on vast amounts of data. Creating a resilient environment that can handle failures and environmental changes without losing days or weeks of model training progress is an operational challenge that requires you to […]

The risks associated with generative AI have been well-publicized. Toxicity, bias, escaped PII, and hallucinations negatively impact an organization’s reputation and damage customer trust. Research shows that not only do risks for bias and toxicity transfer from pre-trained foundation models (FM) to task-specific generative AI services, but that tuning an FM for specific tasks, on […]

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. Amazon SageMaker Canvas now supports comprehensive data preparation capabilities powered by Amazon SageMaker Data Wrangler. With this integration, SageMaker Canvas provides customers with an end-to-end no-code workspace to prepare data, build and use ML and […]

In the last few years Large Language Models (LLMs) have risen to prominence as outstanding tools capable of understanding, generating and manipulating text with unprecedented proficiency. Their potential applications span from conversational agents to content generation and information retrieval, holding the promise of revolutionizing all industries. However, harnessing this potential while ensuring the responsible and […]

In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. However, the increasing cost associated with training and fine-tuning these models poses a challenge for enterprises. This cost is primarily driven by the […]

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio. With this launch, you can programmatically run notebooks as jobs […]

Today, Amazon SageMaker launches a new version (0.25.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs) and adds support for NVIDIA’s TensorRT-LLM Library. With these upgrades, you can effortlessly access state-of-the-art tooling to optimize large language models (LLMs) on SageMaker and achieve price-performance benefits – Amazon SageMaker LMI TensorRT-LLM DLC reduces latency by 33% […]

Generative artificial intelligence (generative AI) models have demonstrated impressive capabilities in generating high-quality text, images, and other content. However, these models require massive amounts of clean, structured training data to reach their full potential. Most real-world data exists in unstructured formats like PDFs, which requires preprocessing before it can be used effectively. According to IDC, […]

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. This is the third post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. In Part 1 and Part 2, we show how the Salesforce Data Cloud and Einstein Studio integration with SageMaker allows businesses to access their […]