One lifecycle, two operating disciplines
MLOps makes software, data, features, experiments, models, deployment, monitoring, and retraining traceable and reversible. LLMOps extends the same release discipline to prompts, model and adapter selection, evaluation corpora, judge configuration, context and token behavior, multi-model serving, GPU capacity, safety, and cost. The objective is not the largest platform. It is a small system whose behavior and release identity can be explained.
Reproducibility, versioning, and experiment evidence
Weeks 1-2Build an immutable chain from source and data to metrics and artifacts. Treat every behavior-changing input as a release dependency.
- Record code revision, parameters, metrics, artifacts, dependencies, seeds, and runtime for each experiment
- Version datasets by immutable snapshot or digest, schema, source, time window, and transformation revision
- Link metrics to the exact dataset and model checkpoint they evaluate
- Create model signatures and representative input examples
- Use MLflow experiments, runs, logged models, and a database-backed Model Registry
- Register model candidates as distinct versions rather than overwriting artifacts
- Use model aliases and validation tags instead of relying on deprecated fixed stages
- Version prompts, response schemas, model parameters, and route manifests
- Pin Hugging Face model, tokenizer, adapter, and dataset revisions
- Write model and dataset cards with intended use, licenses, limitations, provenance, and evaluations
Data, feature, CI/CD, and continuous-training contracts
Weeks 3-4Join deterministic software tests with statistical gates. Continuous training creates candidates; it does not grant automatic production authority.
- Define schema, type, range, null, freshness, category, uniqueness, and relationship constraints
- Test point-in-time correctness and block target or future-data leakage
- Prove parity between training and serving transformations with golden records
- Run unit, integration, contract, bounded training, signature, and packaging tests in CI
- Compare candidates with a versioned baseline using declared tolerances and segments
- Scan dependencies, containers, serialized artifacts, licenses, and provenance
- Separate build, train, evaluate, register, deploy, approve, and rollback permissions
- Make training and release workflows idempotent and observable
- Trigger CT from validated data arrival or approved evidence, not a blind schedule alone
- Require the full release gate before any CT candidate can receive traffic
Kubernetes model serving, monitoring, and rollback
Weeks 5-7Bind immutable model and runtime identities to a measurable serving contract, then release with a known-good revision still available.
- Package dependencies and preprocessing with the exact serving release
- Pin image digest, model version, KServe runtimeVersion, protocol, and configuration
- Configure requests, limits, startup and readiness behavior, timeouts, and restricted network access
- Measure request success, latency, saturation, version identity, and traffic share
- Trace validation, preprocessing, inference, response shaping, and monitoring operations
- Store only governed prediction and outcome evidence with explicit retention
- Compare current and reference data with calibrated drift methods and thresholds
- Monitor nulls, schema, ranges, categories, prediction behavior, delayed labels, and segments separately
- Canary a candidate and stop immediately when a release threshold breaches
- Reconcile registry, traffic, workloads, caches, and live telemetry during rollback
- Complete the versioned ML delivery and monitoring project
LLM evaluation, tracing, and multi-model serving
Weeks 8-9Evaluate complete LLM releases—not model names alone—and operate prompt, model, adapter, route, token, and GPU behavior as one versioned system.
- Create versioned representative, edge, long-context, structured-output, safety, and adversarial synthetic cases
- Separate datasets, prediction functions, deterministic scorers, judge scorers, and human review
- Calibrate LLM judges against labeled examples and record disagreement and configuration
- Measure task quality, grounding, citations, abstention, refusal, injection resistance, and slices
- Serve pinned models through authenticated OpenAI-compatible vLLM endpoints
- Validate base-model and LoRA adapter compatibility, provenance, isolation, and quotas
- Disable or administrator-restrict dynamic adapter loading
- Trace prompt loading, optional retrieval, queueing, inference, streaming, and post-processing
- Record safe release IDs, tokens, TTFT, output-token latency, errors, queue time, and cost
- Check current OpenTelemetry GenAI convention stability and apply content minimization
Cost, safe rollout, governance, and lifecycle operations
Weeks 10-12Decide releases with quality, safety, reliability, resource, and cost evidence. Prove the platform can stop, recover, and retire.
- Profile prompt and output lengths, concurrency, streaming, adapter mix, arrival patterns, and failure load
- Measure TTFT, output-token latency, throughput, queueing, GPU utilization, memory, and KV-cache limits
- Use parallelism only after measuring model fit, communication, latency, and throughput trade-offs
- Calculate cost per request and cost per acceptable outcome by release and route
- Progress through offline, shadow, and bounded canary gates before promotion
- Exercise a kill switch and restore an immutable known-good manifest
- Apply NIST AI RMF Govern, Map, Measure, and Manage across the lifecycle
- Review third-party models, adapters, data, licenses, remote code, identities, and artifact integrity
- Run incident, rollback, retention, access-review, and decommissioning exercises
- Complete the multi-model LLM serving, evaluation, cost, and safe rollout project
- Publish measured limits without claiming universal safety or production scale
PrepKloud MLOps & LLMOps learning surfaces
Practical scenarios across lineage, CI/CD/CT, quality, KServe, drift, LLM evaluation, tracing, cost, security, and rollback. 25 engineering flashcards
Review registries, versioning, feature parity, canaries, drift, prompts, vLLM, OpenTelemetry, cost, and governance. Two portfolio projects
Build a versioned ML pipeline and a governed multi-model LLM platform using synthetic data. Complete practical guide
Read the lifecycle, architecture, release gates, observability, evaluation, cost, security, and project strategy. DevOps for AI applications
Connect model operations to broader software delivery and reliability practice. OpenTelemetry engineering guide
Go deeper on context, traces, metrics, Collector policy, cardinality, privacy, and troubleshooting. Explore related roles
Research current ML platform, AI platform, MLOps, LLMOps, and reliability role requirements without guaranteed outcomes. Editorial policy
Review sourcing, originality, independence, update, and correction standards.
Official grounding
Use official Tracking, dataset, model signature, Model Registry, Prompt Registry, evaluation, tracing, and deployment documentation.
Open MLflow docsGround workload rollout, revision history, resources, model runtimes, InferenceService, canary traffic, and rollback in current docs.
Open Kubernetes DeploymentsOpen KServe docs
Use traces, context propagation, security, sensitive-data handling, and current GenAI convention status for model and LLM observability.
Open OpenTelemetry docsStudy current/reference comparisons, drift methods and thresholds, text drift, dataset drift share, null handling, and report customization.
Open Evidently drift guideReview repository revisions, model and dataset cards, metadata, licenses, limitations, base-model relationships, and artifact security.
Open Hugging Face Hub docsUse official OpenAI-compatible serving, LoRA, security, optimization, and scaling documentation for the LLM serving project.
Open vLLM docsApply Govern, Map, Measure, and Manage plus the Generative AI Profile to lifecycle and deployment decisions.
Open NIST AI RMFFrequently asked questions
Is this MLOps and LLMOps path a certification?
No. It is explicitly a practical skill path. It claims no credential, exam, passing score, official blueprint, or marketplace question source. Progress is demonstrated with release evidence and two synthetic-data projects.
Should MLOps and LLMOps be learned separately?
They share versioning, data and software quality, CI/CD, serving, observability, security, governance, cost, and rollback. Learn that base once, then add prompt, evaluation, token, GPU, adapter, safety, and multi-model concerns for LLM systems.
Do I need production data or a large GPU?
No production data is required. Both projects use generated records and prompts. Use small license-compatible models and a short disposable GPU session; a CPU-only reduction is acceptable if latency and capacity limitations are stated honestly.
Does drift automatically mean retraining?
No. Drift is a configured comparison between current and reference distributions. Check missingness, schema, reference relevance, segments, predictions, and delayed labeled performance before choosing investigation, labeling, retraining, rollback, or no action.
What projects complete the path?
The first builds a versioned ML model delivery and monitoring pipeline with MLflow, KServe, drift, canary, rollback, and controlled CT. The second builds a multi-model vLLM platform with versioned prompts, synthetic evaluations, tracing, GPU cost, shadowing, canary rollout, kill switch, rollback, and governance.
Build a release system that can explain itself
Version every behavior-changing input, evaluate complete releases, observe the serving path, measure cost, exercise failure, and prove rollback before calling a model ready.