This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. The Salesforce AI Model Serving team is working to push the boundaries of natural language processing and AI capabilities for enterprise applications. Their keyContinue Reading
Deploying models efficiently, reliably, and cost-effectively is a critical challenge for organizations of all sizes. As organizations increasingly deploy foundation models (FMs) and other machine learning (ML) models to production, they face challenges related to resource utilization, cost-efficiency, and maintaining high availability during updates. Amazon SageMaker AI introduced inference componentContinue Reading
In enterprise environments, organizations often divide their AI operations into two specialized teams: an AI research team and a model hosting team. The research team is dedicated to developing and enhancing AI models using model training and fine-tuning techniques. Meanwhile, a separate hosting team is responsible for deploying these modelsContinue Reading
This post is co-written with Isaac Smothers and James Healy-Mirkovich from Crexi. With the current demand for AI and machine learning (AI/ML) solutions, the processes to train and deploy models and scale inference are crucial to business success. Even though AI/ML and especially generative AI progress is rapid, machine learningContinue Reading
AlbertPego/iStock via Getty Images Golar LNG (NASDAQ:GLNG) +10.3% pre-market Friday after saying it entered into definitive agreements with Pan American Energy (OTCQB:PAANF) for a 20-year deployment of a floating liquefied natural gas vessel in Argentina, tapping into the Vaca Muerta shale formation in the Neuquina Basin, the world’s second-largest shaleContinue Reading
