GenAIOps & MLOps for LLM Applications

OVERVIEW

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.

What Organizations Gain

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

What you’ll learn

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

Prerequisites

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.

Curriculum

Structured for Strategic Application

Module 1 — GenAIOps/LLMOps foundations and operating model (2 hours)

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.

Module 2 — Building LLM apps with prompt flow: experimentation, variants, deployment (4 hours)

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.

Module 3 — Offline evaluation: datasets, metrics, and CI quality gates (5 hours)

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).

Module 4 — Production monitoring: quality, safety, token usage, and operational KPIs (4 hours)

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.

Module 5 — Continuous evaluation and observability for agents and advanced LLM apps (3 hours)

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.

Module 6 — Safety operations and governance: content filters, default safety, and change control (3 hours)

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.

Module 7 — Release engineering for LLM apps: canary, A/B, rollback, and lifecycle management (2 hours)

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.

Module 8 — Production readiness: SLOs, runbooks, and cost/performance optimization (1 hour)

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”.

Capstone — End-to-end GenAIOps pipeline for an LLM application (Optional, 2–3 hours)

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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Feature

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.

Recommended participant setup

This course follows Cognixia’s AI-first, hands-on learning model

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.

INTERESTED IN THIS COURSE?

Let’s Connect

Speak with a Cognixia specialist about enrollment options, custom cohorts for your leadership team, or tailored delivery formats for your organization.

Response within 1 business day
Available in 5 delivery formats globally
Volume pricing for teams of 10+

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FAQs

Frequently Asked Questions

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

Is this course technical?

Yes. The course is operations-focused and assumes familiarity with CI/CD, cloud platforms, and applied AI systems.

Do participants need prior experience?

Participants should have prior exposure to software delivery pipelines and basic AI or ML concepts to fully benefit.

Is this suitable for enterprise rollout?

Yes. The course is designed for consistent, repeatable adoption across teams and large enterprise environments.

How hands-on is the training?

Approximately 60–70% of the course is hands-on, including pipelines, evaluation workflows, monitoring, and incident drills.

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Why Cognixia

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

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Mapped Official Learning

ntermediate to Advanced