16 articles
Articles, guides and practical notes about AI & Emerging Technology, with useful context on what matters, how it works and where the interesting details are.
AWS AI can suit teams with sound AWS practices, usable data and a bounded problem. But Bedrock or SageMaker AI access alone does not establish readiness: costs, security, governance, skills, lock-in and measurable value all need testing.
The enterprise rush to deploy AI agents is running into a familiar problem in a new form: identity. If an agent can act, call tools, move data or trigger workflows, it needs a proper identity model, not a vague service account and a hope that logs will be enough. That shifts the discussion from ‘what can the model do?’ to ‘what can this agent prove it is allowed to do, and how is that enforced?’
AWS AI services address different jobs: Amazon Bedrock for generative applications, SageMaker AI for custom machine learning, pre-built APIs for recognised tasks, and Amazon Q for employee and developer assistance.
AWS AI is a portfolio rather than a single product. This guide explains where Amazon Bedrock, SageMaker AI, Amazon Q and specialist services fit, and how to choose between foundation models, machine learning platforms, assistants and managed APIs.
Digital Jersey’s Let’s Build AI Project Lab helps teams turn defined use cases into working prototypes, with data, governance and deployment questions addressed before an AI experiment is treated as a production project.
Microsoft Foundry Hosted agents on the initial public-preview backend reach their final documented support date today. Microsoft will not migrate them automatically, leaving teams to redeploy, rework identities and endpoints, and test session behaviour before cutover.
