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How to red team AI agents

Test AI agent permissions, memory, handoffs, retries, and tool actions with a practical security matrix and reproducible evaluation workflow.

Prompt versioning for production AI systems

Treat prompts as versioned production artifacts with lineage, evaluations, promotion workflows, rollback, ownership, and runtime observability.

From notebooks to repeatable ML jobs

Move notebook experiments into repeatable ML jobs with explicit inputs, versioned code, reproducible containers, and Polyaxon tracking.

Scale agentic AI without breaking the infrastructure

Scale AI agents with admission control, dependency-aware concurrency, durable state, bounded authority, backpressure, and outcome-based capacity planning.

What is GPU orchestration?

Understand how GPU orchestration connects workflows, queues, resource placement, and recovery across shared ML infrastructure.

How to test prompt injection in LLM applications

Build prompt injection tests for user input, retrieved documents, and tool responses, with checks for data access and actual side effects.

LLM cost monitoring: Measure cost per successful task

Connect tokens, model calls, retrieval, tools, retries, and infrastructure with quality and task outcomes to control production LLM costs.

Use kubectl edit safely

Use kubectl edit for deliberate live Kubernetes changes while avoiding controller conflicts, configuration drift, wrong-cluster edits, and unrecoverable fixes.

Kubernetes CPU limits and throttling overview

Understand Kubernetes CPU requests, limits, throttling, and the failure modes caused by weak resource configuration.

Verified data pipelines for reliable AI agents

Design agent data paths that preserve source identity, versions, permissions, freshness, retrieval evidence, and verification results.

Offline vs. online evaluation for generative AI

Use offline evaluation for reproducible release decisions and online evaluation for real production behavior, then connect both in one feedback loop.

GPU sharing on Kubernetes: MIG vs. time-slicing

Compare NVIDIA MIG and GPU time-slicing on Kubernetes, including memory isolation, resource names, scheduling behavior, and workload fit.

Manage Kubernetes contexts safely

Use explicit kubeconfig contexts, namespaces, identities, and verification checks to reduce wrong-cluster changes across development, staging, and production.

Design reliable ML pipeline orchestration

Design ML pipelines with explicit dependencies, resource placement, safe caching, bounded retries, and complete evaluation evidence in Polyaxon.

LLM-as-a-judge: Design and validate model-based evaluators

Use LLMs as scalable evaluators without treating them as ground truth: design clear rubrics, calibrate against humans, and monitor bias and drift.

What is an AI sandbox? Development environments and execution isolation

Understand AI sandboxes for development and agent execution, including runtime isolation, credentials, storage, network access, GPUs, and lifecycle.

Kubernetes probes for ML services

Configure startup, readiness, and liveness probes for model servers and interactive ML services without causing restart loops or hiding dependency failures.

Turn production traces into regression tests

Convert representative production failures into sanitized, reproducible evaluation cases that protect future LLM and agent releases.

GPU jobs stuck Pending on Kubernetes: a debugging guide

Diagnose Pending GPU jobs by checking queue admission, scheduler events, advertised GPU resources, placement constraints, storage, and node capacity.

Kubernetes RBAC for ML workloads

Design least-privilege Kubernetes access for ML workloads with clear subjects, namespaced roles, dedicated service accounts, permission checks, and reviewable policy.

Build an ML knowledge repository your team can reuse

Connect experiment records, dataset versions, model artifacts, and review decisions into a reusable ML knowledge repository with Polyaxon.

How to evaluate RAG systems

Evaluate retrieval and generation separately and end to end with representative datasets, groundedness checks, retrieval metrics, and production feedback.

What is AI red teaming?

Design scoped adversarial tests for AI applications, examine tool and retrieval boundaries, and turn findings into regression coverage.

Build a model trust pipeline

Establish model trust through provenance, artifact inspection, reproducible evaluation, approval, runtime verification, monitoring, and rapid revocation.