Operations Engineering Training

GenAIOps & MLOps for LLM Applications

Production-grade GenAIOps and LLMOps practices to deploy, monitor, evaluate, and govern enterprise-scale LLM and agentic applications with reliability and control.
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  • Level : ntermediate to Advanced
  • Duration : 24 hours
  • Hands-on Ratio : 60–70%
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Building Strategic Influence in Matrix Organizations

Operational excellence is critical for sustaining GenAI in production. GenAIOps & MLOps for LLM Applications equips teams to manage, monitor, and continuously improve GenAI systems across their lifecycle.

The course focuses on deployment pipelines, prompt and model versioning, monitoring, cost management, and incident handling for LLM-based applications. Participants learn how traditional MLOps practices adapt to GenAI-specific challenges.

By the end of the course, teams are prepared to operate GenAI systems with reliability, accountability, and performance transparency in enterprise environments.

Recommended participant setup

Azure subscription, Microsoft Foundry access, Azure Monitor and Log Analytics, CI/CD repository, sample application and evaluation datasets

AI-First Learning Approach

This course follows Cognixia’s AI-first, hands-on learning model—combining short concept sessions with practical labs, real workplace scenarios, and embedded governance to ensure safe, scalable, and effective skill adoption across the enterprise.

Business Outcomes

Organizations enrolling teams in this course can achieve

  • Improved Operational Reliability: Faster, safer releases of LLM applications through CI/CD pipelines, evaluation gates, and structured rollback strategies
  • Reduced Risk and Stronger Governance: Built-in safety controls, monitoring, and incident response processes for production GenAI systems
  • Scalable Enterprise Adoption: Standardized GenAIOps frameworks that enable consistent deployment, monitoring, and ROI measurement across teams

Why You Shouldn’t Miss this course

By the end of this course, participants will be able to:
  • Understand / Explain GenAIOps and LLMOps operating models, lifecycle stages, and enterprise failure modes
  • Apply CI/CD workflows for prompts, models, agents, and evaluation pipelines in real enterprise environments
  • Analyze / Evaluate quality, safety, cost, and performance metrics across offline and production LLM workloads
  • Create monitoring dashboards, evaluation reports, and operational runbooks for LLM applications
  • Implement repeatable, governance-ready GenAIOps practices that support enterprise-scale AI adoption

Recommended Experience

Participants are expected to have working knowledge of CI/CD fundamentals and basic programming experience in Python or .NET. Familiarity with cloud platforms, monitoring concepts, and foundational security practices will help learners apply operational and governance concepts effectively in enterprise environments.

Structured for Strategic Application

Bloom-aligned objectives
  • Understand: what makes GenAIOps distinct from classic MLOps
  • Analyze: key failure modes (prompt regressions, retrieval drift, safety violations, tool errors)
  • Design: an operating model (roles, artifacts, gates, KPIs)
Topics
  • Lifecycle overview for LLM applications: prompt/flow engineering, evaluation, deployment, monitoring, governance
  • Artifacts to version: prompts, flows, evaluator configs, datasets, tool schemas, system policies
  • Environment strategy: dev/test/prod separation and controlled promotion
  Labs
  • Lab 1.1: Operating model blueprint — Define artifacts, owners, release gates, and KPIs for a chosen LLM app (RAG assistant or tool-using agent).
  • Lab 1.2: Repo structure standard — Create a baseline repository layout for flows, evaluation sets, and CI pipelines.
Bloom-aligned objectives
  • Apply: prompt flow to build executable LLM app workflows
  • Create: variants and controlled experiments
  • Analyze: experiment results and choose a promotion candidate
Topics
  • prompt flow capabilities in Microsoft Foundry: prototype, iterate, and deploy AI applications via executable flows
  • Flow composition: prompts + Python tools + external calls (retrieval/tools)
  • Variant management: prompt variants, parameters, environment configs
  • Template-driven GenAIOps: Microsoft GenAIOps prompt flow template (repo scaffold, lifecycle management concepts)
Labs
  • Lab 2.1: Build a baseline flow — Implement a prompt flow for a question-answering or RAG workflow with structured outputs.
  • Lab 2.2: Variant experiment — Create 3 prompt variants and compare outcomes on a small test set; document selection criteria.
Lab 2.3: Deploy a flow — Package and deploy the selected flow, capturing deployment config as code.
Bloom-aligned objectives
  • Create: evaluation datasets (golden + adversarial)
  • Implement: automated offline evaluation in CI/CD
  • Evaluate: changes using quality/safety metrics before release
Topics
  • Offline evaluation concepts: measuring quality/safety metrics on test datasets before production
  • Evaluation in CI/CD: GitHub Actions approach for running evaluations and producing reports
  • Evaluator selection: quality metrics (relevance, coherence, fluency) and safety metrics
  • Statistical considerations: meaningful improvement vs random variation (confidence/consistency expectations)
  • Optional enterprise pipeline pattern: Azure DevOps end-to-end GenAIOps with prompt flow (concepts transferable to Foundry-centered pipelines)
Labs
  • Lab 3.1: Golden set builder — Create an evaluation dataset with expected characteristics and failure labels.
  • Lab 3.2: GitHub Actions evaluation gate — Implement a workflow that runs offline evaluation on pull requests and blocks merge on regression.
  • Lab 3.3: Promotion decision report — Generate a standardized evaluation report (metrics + samples + failure clusters + go/no-go).
Bloom-aligned objectives
  • Implement: monitoring for quality and token usage in production
  • Analyze: live telemetry to detect regressions and drift
  • Create: alerting policies and SLOs
Topics
  • Monitoring deployed prompt flow applications: collect inference data and monitor quality/safety metrics and token usage
  • Operational metrics: request counts, latency, error rate; recurring monitoring and alerts
  • Safety telemetry: abuse monitoring components and signals (content classification contributing to monitoring)
  • Dashboard design: release impact view (before/after), cohort analysis, top failure intents
Labs
  • Lab 4.1: Telemetry instrumentation — Add structured logging (prompt version, flow version, tokens, latency, error codes) and route to dashboards.
  • Lab 4.2: Quality monitoring setup — Configure monitoring for groundedness/coherence/relevance (or equivalent metrics) and define alert thresholds.
  • Lab 4.3: Regression triage drill — Simulate a regression (token spike + quality drop), identify root cause, and propose rollback.
Bloom-aligned objectives
  • Understand: continuous evaluation sampling and tradeoffs
  • Apply: near real-time quality/safety evaluation on live traffic
  • Evaluate: agent behaviors using trace-linked diagnostics
Topics
  • Continuous evaluation for agents:
    • near real-time observability at a sampling rate with metrics surfaced in an observability dashboard
    • evaluation results connected to traces for debugging and root cause analysis
  • Agent evaluation via SDK:
    • converting agent thread data into evaluation-ready data for evaluators
  • Operationalization:
    • sampling policies, privacy considerations, and cost management
Labs Lab 5.1: Enable continuous evaluation — Configure continuous evaluation sampling and verify metrics + traces for a sample agent/app. Lab 5.2: Agent run evaluation via SDK — Convert agent thread/run data and run an evaluator; produce an analysis summary.
Bloom-aligned objectives
  • Apply: platform safety controls for Azure OpenAI usage
  • Design: governance for safety configuration changes
  • Evaluate: application behavior under unsafe inputs and policy constraints
Topics
  • Azure OpenAI content filtering:
    • filters applied to prompts and completions to detect harmful content
    • severity thresholds and approval requirements for turning filters down/off
  • Default safety policies:
    • default safety configurations and features applied broadly to models
  • Safety ops playbooks:
    • incident categories (harmful content, injection attempts, data leakage)
    • audit logging and review workflows
Labs
  • Lab 6.1: Safety configuration review — Define a change-control process for content filter modifications (approvals, testing, rollback).
  • Lab 6.2: Safety regression tests — Build an adversarial prompt set and run it in CI; block releases on safety regression.
Bloom-aligned objectives
  • Create: release strategies for LLM apps and agents
  • Apply: canary/A/B with measurable success criteria
  • Analyze: rollout decisions using evaluation + monitoring signals
Topics
  • Release strategies: canary release, shadow testing, A/B experimentation
  • Versioning strategy: prompt versioning, flow versioning, evaluator versioning
  • Rollback discipline: rapid rollback triggers based on monitored metrics
Labs
  • Lab 7.1: Release plan — Create a rollout plan with explicit gates (offline eval pass + monitoring thresholds).
  • Lab 7.2: A/B analysis drill — Compare two flow versions using an evaluation report and a monitoring slice; decide promotion vs rollback.
Bloom-aligned objectives
  • Analyze: cost and latency drivers (tokens, retries, tool calls)
  • Implement: practical optimization levers
  • Create: runbooks for common incidents
Topics
  • Cost controls: token budgeting, context trimming, caching, rate limiting
  • Reliability: retries, circuit breakers for external tools, fallback responses
  • Runbooks: incident response, postmortems, regression prevention
Labs
  • Lab 8.1: Cost guardrails — Implement token caps and alerting on token spikes.
  • Lab 8.2: Ops runbook — Create a concise runbook for “quality drop”, “token spike”, and “safety incident”.
Deliverable A working LLM app (prompt flow + API) with:
  • versioned artifacts in Git
  • CI pipeline that runs offline evaluation and blocks regressions
  • production monitoring for quality + token usage + operational KPIs
  • a governance plan for safety controls and rollback procedures
Tools and platforms used
  • Microsoft Foundry prompt flow (build, iterate, deploy)
  • Microsoft Foundry evaluations + CI integration (GitHub Actions evaluation)
  • Microsoft Foundry observability / continuous evaluation (sampling, metrics, trace linkage)
  • Monitoring quality/safety and token usage for deployed flows
  • Azure OpenAI safety controls (content filtering, default safety policies, abuse monitoring)
  • Optional pipeline patterns: Azure DevOps integration concepts with prompt flow
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Why Cognixia for This Course

  • Direct focus on operating, monitoring, and governing real-world LLM and agentic applications in enterprise environments
  • Hands-on, outcome-driven delivery using production-grade tools, pipelines, and monitoring patterns
  • Responsible and secure-by-design approach with embedded governance, safety controls, and compliance awareness
  • Proven experience delivering large-scale, enterprise AI upskilling and transformation programs globally

Mapped Official Learning

GenAIOps & MLOps for LLM Applications - Course @cognixiaGenAIOps & MLOps for LLM ApplicationsIot-Security-and-Penetration-TestingIoT Security and Penetration TestingGITGIT-Version ControlMavenMavenMicrosoft-Exchange-ServerMicrosoft Exchange Server
Explore Trainings

Designed for Immediate Organizational Impact

Includes real-world simulations, stakeholder tools, and influence models tailored for complex organizations.

Instructor-Led Enterprise Training Expert-led sessions guide participants through real GenAIOps challenges, release strategies, and operational decision-making.
Enterprise-Ready Use Cases Hands-on scenarios mirror real production environments, including monitoring, evaluation, and incident response for LLM systems.
High Hands-On Learning Ratio Participants build pipelines, evaluation gates, dashboards, and runbooks through guided labs and simulations.
Responsible & Scalable AI Adoption Governance, safety, observability, and cost controls are embedded to support long-term, enterprise-scale GenAI deployment.

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Frequently Asked Questions

Find details on duration, delivery formats, customization options, and post-program reinforcement.

Yes. The course is operations-focused and assumes familiarity with CI/CD, cloud platforms, and applied AI systems.
Participants should have prior exposure to software delivery pipelines and basic AI or ML concepts to fully benefit.
Yes. The course is designed for consistent, repeatable adoption across teams and large enterprise environments.
Approximately 60–70% of the course is hands-on, including pipelines, evaluation workflows, monitoring, and incident drills.
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