An MLOps stack spans six layers, each with multiple tool choices: Data layer — data versioning (DVC, LakeFS), feature stores (Feast, Tecton), data quality (Great Expectations). Training layer — experiment tracking (MLflow, W&B), orchestration (Kubeflow, Airflow, Prefect), compute (cloud GPUs, Kubernetes). Registry layer — model registry (MLflow, SageMaker), model cards, artifact storage. Deployment layer — serving (Triton, vLLM, BentoML), CI/CD (GitHub Actions, CML), deployment strategies (canary, blue-green). Monitoring layer — drift detection (Evidently, NannyML), performance monitoring (Prometheus, Grafana), LLM observability (Langfuse). Platform layer — end-to-end platforms that bundle multiple layers (SageMaker, Vertex AI, Databricks).

Amazon SageMaker is the most mature end-to-end ML platform, covering the entire lifecycle: data labeling (Ground Truth), notebook environments (Studio), training (managed training jobs with spot instances), experiment tracking, model registry, deployment (real-time endpoints, batch transform, serverless inference), and monitoring (Model Monitor). Strengths: deepest AWS integration (S3, IAM, VPC, CloudWatch), widest compute options (p5 instances with H100 GPUs, Inferentia/Trainium custom chips), mature feature set (model cards, bias detection with Clarify, feature store), and SageMaker Pipelines for ML workflow orchestration. Best for: teams already on AWS who want a single-vendor solution with enterprise security (VPC isolation, KMS encryption).

Google Vertex AI prioritizes developer ergonomics and data-centric workflows. It integrates tightly with BigQuery (query data directly into training), Vertex AI Feature Store (managed, low-latency feature serving), and Vertex AI Pipelines (based on Kubeflow Pipelines). Strengths: faster iteration (less boilerplate than SageMaker), strong AutoML (best-in-class for tabular, image, and text), Model Garden (one-click deployment of open models like Gemma, Llama), and Vertex AI Agent Builder (build and deploy AI agents with grounding). Best for: teams on GCP, data-heavy workloads with BigQuery, and teams that want fast experimentation with lower operational friction.

Databricks Mosaic AI provides a unified environment for data engineering, ML, and AI on top of the lakehouse architecture (Delta Lake). Strengths: Unity Catalog (unified governance for data, models, and features), MLflow integration (Databricks created MLflow, so integration is seamless), native vector search (for RAG applications), compound AI systems (build agents with models, retrievers, and tools), and multi-cloud (runs on AWS, Azure, and GCP). Best for: large enterprises with data-heavy workloads, teams that want data engineering and ML on the same platform, and organizations that need consistent governance across data and models.

Instead of a managed platform, you can build an MLOps stack from open-source components: MLflow (experiment tracking + model registry — the universal default), Kubeflow Pipelines (ML workflow orchestration on Kubernetes, with per-step artifact tracking and GPU scheduling), DVC (data versioning, works like Git for data), Feast (feature store, offline + online serving), Evidently (monitoring + drift detection), BentoML or KServe (model serving), and Great Expectations (data quality). Advantages: no vendor lock-in, full control, cost-effective (no platform fees). Disadvantages: integration burden (you glue everything together), operational overhead (you manage the infrastructure), and slower to start.

The right stack depends on your context, not on feature comparisons. Key decision factors: Cloud strategy — single-cloud? Use the native platform (SageMaker/Vertex). Multi-cloud? Use Databricks or open-source. Team size and skills — small team? Use a managed platform. Large platform team? Open-source gives more control. Data architecture — already on BigQuery? Vertex AI. Lakehouse? Databricks. S3-heavy? SageMaker. Compliance requirements — strict audit needs? Managed platforms have built-in compliance. Budget — platform fees vs. engineering time. Speed vs. control — need to ship fast? Managed. Need full customization? Open-source. The hardest production challenges are typically data contracts and change management, not the platform itself.

Don’t build the full stack on day one. Start with the minimum viable MLOps and add layers as your needs grow: Phase 1 (Week 1–2): Experiment tracking (MLflow) + model registry. This alone prevents the “which model was best?” problem. Phase 2 (Month 1): Add CI/CD for model training and deployment. Automate the path from code to production. Phase 3 (Month 2–3): Add monitoring (Evidently) and data quality checks (Great Expectations). Catch drift before users do. Phase 4 (Month 3–6): Add feature store, advanced serving (canary deployments), and continuous training. Phase 5 (Month 6+): Add LLMOps layer (gateway, prompt management, guardrails) if using LLMs. Each phase should deliver value before moving to the next.

Over 10 chapters, we’ve covered the full MLOps & LLMOps lifecycle: Ch 1–2: Why MLOps matters and experiment tracking (the foundation). Ch 3–4: Model registry, data versioning, data pipelines, and feature stores (the data layer). Ch 5–6: CI/CD for ML and model serving (the deployment layer). Ch 7–8: LLMOps — gateways, routing, prompt management, evaluation, and guardrails (the LLM layer). Ch 9: Monitoring and drift detection (the feedback loop). Ch 10: The full MLOps stack and how to choose and build it incrementally. The key theme: start simple, automate incrementally, and always close the feedback loop from production back to training.