LLMOps Course & Certification

This module of the LLMOps course builds a strong foundation by explaining what makes large language model systems operationally different from traditional machine learning.

MLOps vs LLMOps: What Changes in Production: Understand what carries over from classical MLOps and what changes when prompts, context and retrieval become production artifacts that need their own versioning and testing.

The LLMOps Lifecycle: Learn the define, develop, evaluate, deploy, monitor and optimize phases, and how feedback from production flows back into each stage.

Non-Determinism and Output Variability: Explore why the same prompt can produce different outputs, how sampling parameters affect behavior, and how to design systems that tolerate variability.

Token Economics and AI Cost Drivers: Understand tokenization, input versus output pricing and context window cost, and how usage patterns translate into the monthly AI bill.

Build vs Buy Decisions: Weigh building versus buying models, platforms and tooling, including vendor dependency, data control and total cost of ownership.

Core LLMOps Roles and Terminology: Master the essential vocabulary and understand how AI engineers, MLOps engineers and platform teams share responsibility for LLM systems.

This module of the LLMOps training builds the judgment to choose the right model and serving strategy for latency, cost and scale.

Proprietary APIs vs Open-Weight Models: Compare capability, cost, data control, licensing and operational effort between hosted model APIs and self-hosted open-weight models.

Model Evaluation for Selection: Shortlist candidate models with task-specific benchmarks and weigh response quality against latency and cost targets.

Local Inference Servers with vLLM: Stand up inference servers with OpenAI-compatible endpoints and configure them to handle real production traffic.

GPU Memory and Throughput Bottlenecks: Learn VRAM sizing, KV cache pressure, quantization and batching, and the levers that raise throughput per GPU.

Serving Strategy and Latency Budgets: Plan latency budgets, streaming responses and concurrency limits for user-facing AI features.

Managed, Dedicated and Hybrid Serving: Decide when to use managed endpoints, dedicated GPU capacity or a hybrid approach as usage grows.

This module of the LLMOps certification course turns prompts into versioned, testable production components that stay reliable as applications evolve.

Prompt Architecture and System Prompt Design: Structure roles, instructions, constraints and examples so models behave consistently across thousands of requests.

Dynamic Prompt Templating Systems: Build reusable, version-controlled prompt templates with variables, and promote prompt changes safely across environments.

Enforcing Structured JSON Outputs: Use schemas and function calling to get machine-readable outputs, validate them and retry automatically on malformed responses.

Context Assembly and Window Management: Select, order and compress the most relevant context so it fits within token limits without losing accuracy.

Few-Shot Design and Prompt Optimization: Choose and maintain examples that improve output quality, and measure the impact of each prompt change.

Automated Prompt Regression Testing: Build test suites and expected-behavior checks that catch prompt breakage before it reaches production.

This module of the LLM engineering course connects LLMs reliably to enterprise data through well-engineered ingestion, retrieval and grounding.

RAG Architecture End to End: Map ingestion, embedding, indexing, retrieval, generation and grounding as one production pipeline with clear failure points.

Document Ingestion and Parsing: Parse PDFs, web pages and structured sources cleanly, and enrich chunks with metadata that improves retrieval.

Chunking Strategies: Apply fixed-size, semantic and structure-aware chunking, and test which approach works best for your content.

Embeddings and Vector Databases: Select embedding models, integrate pgvector and Pinecone, and choose index types and similarity metrics.

Hybrid Search and Re-Ranking: Combine keyword and vector search, add re-ranking models and use query rewriting to improve context relevance.

Debugging RAG in Production: Diagnose silent retrieval failures, detect stale data and track retrieval quality metrics over time.

Scaling and Incremental Indexing: Keep indexes fresh with incremental updates, and plan retrieval capacity as data and traffic grow.

This module of the LLMOps course helps you decide when customization is worth the cost and how to apply it efficiently.

Fine-Tune vs Prompt vs RAG: Use clear decision criteria and cost-benefit reasoning to choose the least complex method that meets your quality goals.

Parameter-Efficient Fine-Tuning with LoRA: Train lightweight adapters with LoRA and related methods to customize models with minimal compute.

Training Configuration and Compute Planning: Set key training parameters and estimate the GPU time and cost a fine-tuning run needs.

Dataset Curation and Quality Control: Collect, format and clean training examples, removing duplicates and low-quality data before training.

Synthetic Data Generation: Generate synthetic training data responsibly, and check it for bias and accuracy before use.

Evaluating and Deploying Customized Models: Compare fine-tuned models against baselines, serve adapters efficiently and version them in a model registry.

This module of the LLMOps training replaces guesswork with measurable quality and release gates that protect production.

Evaluation Strategy for LLM Applications: Combine offline and online evaluation, and set quality thresholds that match business risk.

Golden Dataset Construction: Build representative test sets with edge cases and expected outputs, and keep them current as the product changes.

LLM-as-a-Judge Evaluation Patterns: Design scoring rubrics, calibrate automated judges and check them for bias against human review.

Human Evaluation and Feedback Loops: Decide when human review is needed, and turn user feedback into new test cases.

RAG and Output Quality Metrics: Track groundedness, faithfulness, relevance and hallucination rate across releases.

CI Quality Gates: Run automated evaluations in CI pipelines and block releases that degrade performance.

This module of the LLM deployment course packages LLM systems into repeatable, orchestrated deployments that fit existing engineering workflows.

Containerizing LLM Applications: Build Docker images for LLM services, manage dependencies and create GPU-enabled containers for inference.

Kubernetes Orchestration for LLM Workloads: Configure deployments, resource requests, GPU scheduling and autoscaling for variable traffic.

Caching Strategies: Apply response, semantic and prompt caching, and measure cache hit rates and invalidation.

Model Registries and Artifact Management: Version models, prompts and adapters, and promote approved artifacts between environments.

CI/CD for LLM Systems: Build pipelines that test and deploy prompt, RAG and model updates automatically.

Safe Rollouts and Rollback: Use canary releases and fast rollback so a bad change never reaches every user.

This module of the LLMOps certification course makes every request traceable and every dollar of inference spend visible.

Tracing LLM Applications with OpenTelemetry: Instrument chains, retrieval calls and agent steps with span-level traces.

Observability with LangSmith and Phoenix: Build live tracing dashboards and pinpoint whether a failure came from the prompt, retrieval or the model.

Drift, Error Rate and Latency Monitoring: Detect model and data drift, track error rates and latency, and set alert thresholds.

Hallucination Monitoring: Track hallucination signals in production and route risky responses for review.

Cost-per-Query Tracking: Attribute token spend by feature, team or user, and set budget alerts for inference cost.

Cost Optimization with Routing and Semantic Caching: Send simple queries to cheaper, faster models and cache repeated answers to cut spend.

This module of the LLMOps course protects users, data and brand reputation with layered defenses built into the system.

The LLM Threat Landscape: Apply the OWASP Top 10 for LLM applications, map the attack surface and prioritize risks.

Prompt Injection and Jailbreak Defense: Defend against direct and indirect prompt injection, and handle untrusted content safely.

Guardrails with NeMo Guardrails and LlamaGuard: Implement input and output rails that block toxic, off-topic or off-brand responses.

Red-Teaming LLM Applications: Run structured adversarial testing to find vulnerabilities, and track fixes to closure.

PII Detection and Redaction: Detect and redact personal data in prompts, logs and outputs to protect user privacy.

AI Governance and Audit Trails: Set data handling policies, keep audit trails and align LLM operations with governance requirements.

This final module of the LLMOps training brings everything together in a production-scale, multi-model architecture.

Multi-Model Routing Architecture: Route queries by complexity and cost, with fallback cascades and provider abstraction layers.

Disaster Recovery and Resilience: Design multi-provider redundancy, failover, retries, timeouts and circuit breakers.

Scaling Agentic Systems: Run tool-using agents in production, and contain runaway loops and cost.

Performance and Cost at Scale: Balance quality, latency and cost as traffic grows across models and providers.

Production Readiness Review: Check a system against a production checklist covering evaluation, observability, security and cost.

Capstone: Production-Ready AI Application: Architect and deploy a complete agentic system with multi-model routing, disaster recovery, guardrails, evaluation and observability.