Category: Learning Levels

This post is co-written with Stanislav Yeshchenko from Q4 Inc. Enterprises turn to Retrieval Augmented Generation (RAG) as a mainstream approach to building Q&A chatbots. We continue to see emerging challenges stemming from the nature of the assortment of datasets available. These datasets are often a mix of numerical and text data, at times structured, […]

Large language model (LLM) training has become increasingly popular over the last year with the release of several publicly available models such as Llama2, Falcon, and StarCoder. Customers are now training LLMs of unprecedented size ranging from 1 billion to over 175 billion parameters. Training these LLMs requires significant compute resources and time as hundreds […]

Structured data, defined as data following a fixed pattern such as information stored in columns within databases, and unstructured data, which lacks a specific form or pattern like text, images, or social media posts, both continue to grow as they are produced and consumed by various organizations. For instance, according to International Data Corporation (IDC), […]

The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise. From data collection and cleaning to feature engineering, model building, tuning, and deployment, ML projects often take months for developers to complete. And experienced data […]

As organizations scale the adoption of machine learning (ML), they are looking for efficient and reliable ways to deploy new infrastructure and onboard teams to ML environments. One of the challenges is setting up authentication and fine-grained permissions for users based on their roles and activities. For example, MLOps engineers typically perform model deployment activities, […]

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and scalable deployments. SageMaker provides […]

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and scalable deployments. Although […]

Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. Code Editor is based on Code-OSS, Visual Studio Code Open Source, and provides access to the familiar environment and tools of the popular IDE that machine learning (ML) developers know and love, fully integrated […]

As democratization of foundation models (FMs) becomes more prevalent and demand for AI-augmented services increases, software as a service (SaaS) providers are looking to use machine learning (ML) platforms that support multiple tenants—for data scientists internal to their organization and external customers. More and more companies are realizing the value of using FMs to generate […]

As organizations deploy models to production, they are constantly looking for ways to optimize the performance of their foundation models (FMs) running on the latest accelerators, such as AWS Inferentia and GPUs, so they can reduce their costs and decrease response latency to provide the best experience to end-users. However, some FMs don’t fully utilize […]