Author: Shikhar Kwatra

This post demonstrates a proactive approach for security vulnerability assessment of your accounts and workloads, using Amazon GuardDuty, Amazon Bedrock, and other AWS serverless technologies. This approach aims to identify potential vulnerabilities proactively and provide your users with timely alerts and recommendations, avoiding reactive escalations and other damages.

In the ever-evolving landscape of machine learning and artificial intelligence (AI), large language models (LLMs) have emerged as powerful tools for a wide range of natural language processing (NLP) tasks, including code generation. Among these cutting-edge models, Code Llama 70B stands out as a true heavyweight, boasting an impressive 70 billion parameters. Developed by Meta […]

Today, we are excited to announce that the DBRX model, an open, general-purpose large language model (LLM) developed by Databricks, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. The DBRX LLM employs a fine-grained mixture-of-experts (MoE) architecture, pre-trained on 12 trillion tokens of carefully curated data and […]

Every organization has its own set of standards and practices that provide security and governance for their AWS environment. Amazon SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. SageMaker provides a set of templates […]

Identifying, collecting, and transforming data is the foundation for machine learning (ML). According to a Forbes survey, there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. In addition, many of our customers face several challenges during the model operationalization phase […]