Spotlight

How I Built a Production-Grade Object Detection System That Scales Itself

Rushyanth Nerellakunta

This article walks through a real-time object detection pipeline where Kafka absorbs bursty camera traffic, YOLOv8 workers autoscale on Kubernetes, and MLflow tracks every model version.

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Tools and utilities

  • kubectl-netshoot: network troubleshooting plugin for kubectl

    kubectl-netshoot is a kubectl plugin that spins up a netshoot container in a pod or on a node, so you can debug cluster networking without adding any tools to your own images.

  • G-Kubewall

    kubewall provides a simple and rich real time interface to manage and investigate your clusters.

  • Hortator

    Hortator lets AI agents spawn sub-agents at runtime, with each agent running in its own pod with budget caps, network policies, PII redaction, and capability inheritance so children can never escalate beyond their parent's permissions.

  • High-Performance NVMe-oF Storage for Kubernetes

    If you're running I/O-intensive workloads, traditional block storage latency becomes a bottleneck.

  • SnowOps Labs: Hands-On Kubernetes Failure Lab

    SnowOps Labs is a Kubernetes practice lab that builds a local cluster, injects realistic failures, and grades your fixes through guided scenarios and incidents.

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Building Platforms for AI Agents
Building Platforms for AI Agents

Non-deterministic agents pose specific challenges for platform teams in observability, state management, governance, and trust.

Mauricio (Salaboy) Salatino explains why agentic applications behave like distributed multi-agent systems. His test assigned agents to take an order, cook the pizza, deliver it, and charge the customer. One order crossed 15 containers and produced 200 traces.

In this interview:

  • Why agent frameworks can recreate monolith scaling and resource contention
  • How OpenTelemetry data can measure agent behavior and trust over time
  • Why narrowly scoped agents are safer than agents that follow long sequences
  • How the platform can become the learning layer that feeds better context back to LLMs

Learn from production

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Matching jobs

    • Data Engineer with Clear Street

    • Salary: $150K to $250K a year

    • Location: fully remote

    • Tech stack: Kubernetes, Python, SQL, Javascript, Shell, Snowflake, Datadog

    • Data Engineer with Okta

    • Salary: $160K to $220K a year

    • Location: based in the office in Toronto, ON, CA

    • Tech stack: Kubernetes, AWS, Java, Flink, Snowflake, Kafka, Spark, Hadoop

    • Data Engineer with Trace3

    • Salary: $170K to $200K a year

    • Location: remote from

    • Tech stack: Kubernetes, AWS, Azure, Javascript, Python, Shell, SQL, Snowflake, Kafka, Airflow

    • Data Engineer with ZipRecruiter

    • Salary: $130K to $175K a year

    • Location: remote from

    • Tech stack: Kubernetes, Docker, Python, Cassandra, Kafka, Spark, Hadoop

    • DevOps Engineer with CITECH

    • Salary: $131.76K to $423.5K a year

    • Location: based in the office (and remote from home) in Paris, FR

    • Tech stack: Kubernetes, Helm, Shell, SQL

Discover more Kubernetes jobs on Kube Careers →

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