#Deployment

Deployment engineering ensures that systems move from development to production reliably. Articles cover Cloud Run deployment for multi-agent services, A2A endpoint exposure, embed.FS for single-binary Go deployments, and the strategies that make deployments repeatable and rollback-safe.

5 posts tagged with deployment. ← All posts

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Pratik Dhanave · ·6 min read

Deployment Strategies

How you put new code into production is a design decision with real consequences for risk and downtime. Deploy it all at once and a bad release hits everyone; roll it out gradually and you can catch problems while they're small. Blue-green, canary, rolling, and feature flags are the core techniques — each trading complexity for safety in a different way. This post covers when and why to use each.

How you put new code into production is a design decision with real consequences for risk and downtime. Deploy it all at once and a bad release hits everyone; roll it out gradually and you catch problems while they're small. Blue-green, canary, rolling, and feature flags are the core techniques — each trading complexity for safety differently. When and why to use each.

#CI/CD#DevOps#Deployment#Feature Flags#Software Delivery
Pratik Dhanave · ·6 min read

Integration and Deployment in Customer Environments

The demo ran on your laptop with clean data — production means the customer's messy systems, their security rules, and their data as it actually is, which is where most forward deployed work is really won.

Production means the customer's messy data, systems you don't control, and security you must pass: profiling dirty data, loose-coupling integrations, treating security/residency as gates, and deploying + handing over so you can actually leave.

#Forward Deployed Engineer#Integration#Deployment#Data#Forward Deployed Engineering
Pratik Dhanave · ·7 min read

Computer Vision in Practice — Shipping Systems That See

Building a computer-vision system that works in a demo is one thing; building one that works reliably in the real world — at the right speed, cost, and robustness — is another. This closing post assembles the series into practical judgment: how to approach a vision problem, the deployment realities that trip teams up, and where the field is heading. It turns pixels-to-understanding from a concept into something you can actually ship.

Building a vision demo is one thing; shipping a reliable system at the right speed, cost, and robustness is another. A workflow for approaching a vision problem, the deployment realities (efficiency for real-time/edge, robustness to distribution shift, edge cases, monitoring), and where the field is heading — multimodal and foundation models.

#Computer Vision#Deployment#Engineering Judgment#Multimodal AI#Deep Learning#Machine Learning

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.