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handle model updates (versioning, rollback, A/B testing)
Read more ↗1. Mindset: consider models as software services A model is a first-class deployable artifact. It gets treated as a microservice binary: it has versions, contracts in the form of inputs and outputs, tests, CI/CD, observability, and a rollback…
Read more ↗model inference change (on-device, edge, federated)…
Read more ↗1. On-Device Inference: "Your Phone Is Becoming the New AI Server" The biggest shift is that it's now possible to run surprisingly powerful models on devices: phones, laptops, even IoT sensors. Why this matters: No round-trip to the…
Read more ↗generative AI/large-language-models (LLMs) be safely and effectively
Read more ↗1) Why LLMs are different and why they help LLMs are general-purpose language engines that can summarize notes, draft discharge letters, translate clinical jargon to patient-friendly language, triage symptom descriptions, and surface relevant guidelines. Early…
Read more ↗the key interoperability standards e.g., FHIR
Read more ↗1. Some Key Interoperability Standards in Digital Health 1. HL7: Health Level Seven It is one of the oldest and most commonly used messaging standards. Defines the rules for sending data like Admissions, Discharges, Transfers, Lab Results, Billings…
Read more ↗China transitioning diesel trucks to electric vehicles
Read more ↗What’s happening Yes, there are significant shifts underway in both manufacturing and regulation, and the trucking industry in China is a clear case in point: In China, battery-electric heavy-duty trucks are growing rapidly in the share of new…
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1. The Mindset: LLMs Are Not “Just Another API” They’re a Data Gravity Engine When enterprises adopt LLMs, the biggest mistake is treating them like simple stateless microservices. In reality, an LLM’s “context window” becomes a temporary memory,…
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