Tag: Jupyter
Breaking News
Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs | Amazon Web Services
Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook...
Simplify data prep for generative AI with Amazon SageMaker Data Wrangler | Amazon Web Services
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...
Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio | Amazon Web Services
This post is co-written with Marc Neumann, Amor Steinberg and Marinus Krommenhoek from BMW Group. The BMW Group – headquartered in Munich, Germany –...
Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine | Amazon Web Services
The financial service (FinServ) industry has unique generative AI requirements related to domain-specific data, data security, regulatory controls, and industry compliance standards. In addition,...
Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation | Amazon Web Services
Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as repositories,...
Build a foundation model (FM) powered customer service bot with agents for Amazon Bedrock | Amazon Web Services
From enhancing the conversational experience to agent assistance, there are plenty of ways that generative artificial intelligence (AI) and foundation models (FMs) can help...
Implement a custom AutoML job using pre-selected algorithms in Amazon SageMaker Automatic Model Tuning | Amazon Web Services
AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up...
Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker | Amazon Web Services
In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. One of...
Explore advanced techniques for hyperparameter optimization with Amazon SageMaker Automatic Model Tuning | Amazon Web Services
Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. Hyperparameters are the knobs and levers that...
Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD | Amazon Web Services
Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is...
Evasive Jupyter Infostealer Campaign Showcases Dangerous Variant
Security researchers have spotted a recent increase in attacks involving a sophisticated new variant of Jupyter, an information stealer that has been targeting users...
Build a medical imaging AI inference pipeline with MONAI Deploy on AWS | Amazon Web Services
This post is cowritten with Ming (Melvin) Qin, David Bericat and Brad Genereaux from NVIDIA. Medical imaging AI researchers and developers need a scalable,...
Latest Intelligence
