Most agent frameworks observe model calls and allow rewriting them only after they reach the model, making an understanding of callbacks and middleware essential.
No, but its role has fundamentally changed. Here is what I have seen work, after building data platforms at enterprise scale across multiple industries.
Multi-scale feature learning helps CNNs and U-Net models combine global context with fine details, improving accuracy in tasks like image segmentation.
AI models do not fail due to bad coding; they fail due to an upstream change in the input. Combine contracts with circuit breakers to stop bad data from entering models.
A practical guide to SaaS architecture decisions that determine whether platforms scale cleanly or collapse under technical debt, security, and growth pressure.
Use Kong as an API gateway to centralize JWT auth, rate limiting, and access control across all microservices, keeping individual services focused on business logic.
Achieve zero-downtime deployments for Java applications on Kubernetes using rolling updates, readiness/liveness probes, and graceful shutdown strategies.
Design a stateless JWT auth service with Spring Boot 3, Redis caching, and Sentinel for high availability, faster token validation, and reduced DB load.
Docker Hardened Images solve the CVE problem. But CVEs aren't why containers fail in production — governance gaps are. Here's the trust architecture that closes them.
GenAI is easy to prototype but hard to productionize. Vertex AI Agent Builder provides a unified platform for RAG, orchestration, security, and scalable deployment.
Unbounded retries and autoscaling can turn minor latency into cascading outages. API reliability must be bounded and load-aware to prevent retry storms.
GPUStack is an open-source tool that turns a bunch of scattered GPU machines into one managed cluster for deploying AI models behind an OpenAI-compatible API.
Have you ever needed to generate OpenAPI documentation directly from your code and, more importantly, do it in a way that fits cleanly into a CI pipeline?
Docker packages applications to ensure consistent and portable deployments. Kubernetes manages them with scaling, reliability, and automation in production.
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