Favorite This post is cowritten by Salesforce’s AI Platform team members Srikanta Prasad, Utkarsh Arora, Raghav Tanaji, Nitin Surya, Gokulakrishnan Gopalakrishnan, and Akhilesh Deepak Gotmare. Salesforce’s Artificial Intelligence (AI) platform team runs customized large language models (LLMs)—fine-tuned versions of Llama, Qwen, and Mistral—for agentic AI applications like Agentforce. Deploying these

Read More
Shared by AWS Machine Learning October 15, 2025

Favorite The proliferation of Internet of Things (IoT) devices has transformed how we interact with our environments, from homes to industrial settings. However, as the number of connected devices grows, so does the complexity of managing them. Traditional device management interfaces often require navigating through multiple applications, each with its

Read More
Shared by AWS Machine Learning October 15, 2025

Favorite Organizations need solutions for people and AI agents to securely collaborate through a single interface to the organization’s data and take actions across enterprise applications to improve productivity. The ability of an AI agent to securely and seamlessly connect with organizational knowledge bases, enterprise applications, and other AI agents

Read More
Shared by AWS Machine Learning October 14, 2025

Favorite This post was written with Ajay Nyamati from Kitsa. The clinical trial industry conducts medical research studies to evaluate the safety, efficacy, and effectiveness of new drugs, treatments, or medical devices before they reach the market. The industry is a cornerstone of medical innovation, yet it continues to face

Read More
Shared by AWS Machine Learning October 14, 2025

Favorite In healthcare, the ability to quickly analyze and interpret medical reports is crucial for both healthcare providers and patients. While medical reports contain valuable information, they often remain underutilized due to their complex nature and the time-intensive process of analysis. This complexity manifests in several ways: the interpretation of

Read More
Shared by AWS Machine Learning October 14, 2025

Favorite The convergence of artificial intelligence with physical systems marks a pivotal moment in technological evolution. Physical AI, where algorithms transcend digital boundaries to perceive, understand, and manipulate the tangible world, will fundamentally transform how enterprises operate across industries. These intelligent systems bridge the gap between digital intelligence and physical

Read More
Shared by AWS Machine Learning October 14, 2025

Favorite Get agents out of prototype purgatory and into production with security, scalability, and reliability When we launched AWS in 2006, we believed that cloud computing would transform how organizations build and scale technology. We’re now at a similar inflection point with AI agents. We envision a world where billions

Read More
Shared by AWS Machine Learning October 13, 2025

Favorite Consider a growing social media platform that processes millions of user posts daily. Their content moderation team faces a familiar challenge: their rule-based system flags a cooking video discussing “knife techniques” as violent content, frustrating users, while simultaneously missing a veiled threat disguised as a restaurant review. When they

Read More
Shared by AWS Machine Learning October 10, 2025

Favorite This post was written with Dominic Catalano from Anyscale. Organizations building and deploying large-scale AI models often face critical infrastructure challenges that can directly impact their bottom line: unstable training clusters that fail mid-job, inefficient resource utilization driving up costs, and complex distributed computing frameworks requiring specialized expertise. These

Read More
Shared by AWS Machine Learning October 10, 2025

Favorite Machine learning operations (MLOps) is the combination of people, processes, and technology to productionize ML use cases efficiently. To achieve this, enterprise customers must develop MLOps platforms to support reproducibility, robustness, and end-to-end observability of the ML use case’s lifecycle. Those platforms are based on a multi-account setup by

Read More
Shared by AWS Machine Learning October 9, 2025