Scale AI in South Africa using Amazon Bedrock global cross-Region inference with Anthropic Claude 4.5 modelsArtificial Intelligence In this post, we walk through how global cross-Region inference routes requests and where your data resides, then show you how to configure the required AWS Identity and Access Management (IAM) permissions and invoke Claude 4.5 models using the global inference profile Amazon Resource Name (ARN). We also cover how to request quota increases for your workload. By the end, you’ll have a working implementation of global cross-Region inference in af-south-1.
In this post, we walk through how global cross-Region inference routes requests and where your data resides, then show you how to configure the required AWS Identity and Access Management (IAM) permissions and invoke Claude 4.5 models using the global inference profile Amazon Resource Name (ARN). We also cover how to request quota increases for your workload. By the end, you’ll have a working implementation of global cross-Region inference in af-south-1. Read More
Simplify ModelOps with Amazon SageMaker AI Projects using Amazon S3-based templatesArtificial Intelligence This post explores how you can use Amazon S3-based templates to simplify ModelOps workflows, walk through the key benefits compared to using Service Catalog approaches, and demonstrates how to create a custom ModelOps solution that integrates with GitHub and GitHub Actions—giving your team one-click provisioning of a fully functional ML environment.
This post explores how you can use Amazon S3-based templates to simplify ModelOps workflows, walk through the key benefits compared to using Service Catalog approaches, and demonstrates how to create a custom ModelOps solution that integrates with GitHub and GitHub Actions—giving your team one-click provisioning of a fully functional ML environment. Read More
Dark Reading asked readers whether agentic AI attacks, advanced deepfake threats, board recognition of cyber as a top priority, or password-less technology adoption would be most likely to become a trending reality for 2026. Read More
Investors poured $140 million into Torq’s Series D Round, bringing the startup’s valuation to $1.2 billion, to bring AI-based “hyper automation” to SOCs. Read More
Microsoft announced that it will disable the 30-year-old NTLM authentication protocol by default in upcoming Windows releases due to security vulnerabilities that expose organizations to cyberattacks. […] Read More
Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AIArtificial Intelligence Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation,
Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, Read More
A Coding Implementation to Training, Optimizing, Evaluating, and Interpreting Knowledge Graph Embeddings with PyKEENMarkTechPost In this tutorial, we walk through an end-to-end, advanced workflow for knowledge graph embeddings using PyKEEN, actively exploring how modern embedding models are trained, evaluated, optimized, and interpreted in practice. We start by understanding the structure of a real knowledge graph dataset, then systematically train and compare multiple embedding models, tune their hyperparameters, and analyze
The post A Coding Implementation to Training, Optimizing, Evaluating, and Interpreting Knowledge Graph Embeddings with PyKEEN appeared first on MarkTechPost.
In this tutorial, we walk through an end-to-end, advanced workflow for knowledge graph embeddings using PyKEEN, actively exploring how modern embedding models are trained, evaluated, optimized, and interpreted in practice. We start by understanding the structure of a real knowledge graph dataset, then systematically train and compare multiple embedding models, tune their hyperparameters, and analyze
The post A Coding Implementation to Training, Optimizing, Evaluating, and Interpreting Knowledge Graph Embeddings with PyKEEN appeared first on MarkTechPost. Read More
Robbyant Open Sources LingBot World: a Real Time World Model for Interactive Simulation and Embodied AIMarkTechPost Robbyant, the embodied AI unit inside Ant Group, has open sourced LingBot-World, a large scale world model that turns video generation into an interactive simulator for embodied agents, autonomous driving and games. The system is designed to render controllable environments with high visual fidelity, strong dynamics and long temporal horizons, while staying responsive enough for
The post Robbyant Open Sources LingBot World: a Real Time World Model for Interactive Simulation and Embodied AI appeared first on MarkTechPost.
Robbyant, the embodied AI unit inside Ant Group, has open sourced LingBot-World, a large scale world model that turns video generation into an interactive simulator for embodied agents, autonomous driving and games. The system is designed to render controllable environments with high visual fidelity, strong dynamics and long temporal horizons, while staying responsive enough for
The post Robbyant Open Sources LingBot World: a Real Time World Model for Interactive Simulation and Embodied AI appeared first on MarkTechPost. Read More
The Tenable One AI Exposure add-on discovers unsanctioned AI use in the organization and enforces policy compliance with approved tools. Read More
AI2 Releases SERA, Soft Verified Coding Agents Built with Supervised Training Only for Practical Repository Level Automation WorkflowsMarkTechPost Allen Institute for AI (AI2) Researchers introduce SERA, Soft Verified Efficient Repository Agents, as a coding agent family that aims to match much larger closed systems using only supervised training and synthetic trajectories. What is SERA? SERA is the first release in AI2’s Open Coding Agents series. The flagship model, SERA-32B, is built on the
The post AI2 Releases SERA, Soft Verified Coding Agents Built with Supervised Training Only for Practical Repository Level Automation Workflows appeared first on MarkTechPost.
Allen Institute for AI (AI2) Researchers introduce SERA, Soft Verified Efficient Repository Agents, as a coding agent family that aims to match much larger closed systems using only supervised training and synthetic trajectories. What is SERA? SERA is the first release in AI2’s Open Coding Agents series. The flagship model, SERA-32B, is built on the
The post AI2 Releases SERA, Soft Verified Coding Agents Built with Supervised Training Only for Practical Repository Level Automation Workflows appeared first on MarkTechPost. Read More
