09 Advanced Mlops

🧠 Advanced MLOps

🎯 What You’ll Learn

  • Feature store strategy Manage online/offline parity with versioned features.
  • Design feature ingestion, transformation, and materialization pipelines.
  • Govern feature access, quality scores, and ownership metadata.
  • Model registry ops Govern lifecycle from staging to sunset.
  • Enforce promotion workflows with automated checks and approvals.
  • Track lineage across data, models, deployments, and business impact.
  • Scalable serving Handle multi-model deployments and routing.
  • Implement traffic routing (A/B, canary, shadow) across multiple model versions.
  • Optimize resource allocation with serving graphs and autoscaling policies.
  • Automation layers Use metadata-driven triggers for retraining and rollout.
  • Leverage metadata stores to trigger CI/CD, retraining, and compliance workflows.
  • Connect observability with automated remediation playbooks.

📖 Overview

Advanced MLOps extends foundational practices to support complex, multi-team ecosystems. Feature stores deliver consistent features across training and inference, while model registries orchestrate approvals, lineage, and deprecation. Metadata services coordinate retraining triggers, compliance workflows, and automated rollbacks. These capabilities elevate the ML platform from project-based delivery to productized operations.

Scaling to dozens of models requires routing traffic intelligently, isolating resources, and prioritizing models based on business impact. Organizations adopt platform-as-a-service mindsets, providing reusable components, self-service tooling, and governance frameworks. Advanced MLOps also interactions across stakeholders—data scientists, platform engineers, SREs, compliance officers, and product managers.

Platform Capabilities

  • Feature Ops Central feature repository, transformation pipelines, caching, and point-in-time correctness.
  • Model Lifecycle Management Versioning, staging, approvals, promotion policies, and deprecation workflows.
  • Serving Mesh Routing, load balancing, ensemble management, and resource isolation.
  • Metadata & Lineage Capture artifacts, datasets, metrics, approvals, and business outcomes.
  • Automation & Governance Policy engines, triggers, and audit mechanisms enforcing compliance.
  • Developer Experience SDKs, CLI tools, templates, and documentation enabling self-service.

Organizational Prerequisites

  • Strong Foundations Teams already practice version control, CI/CD, monitoring, and incident response.
  • Clear Ownership Responsibilities defined for feature owners, model stewards, and platform operations.
  • Change Management Communication channels and processes for rolling out platform updates.
  • Data Culture Continual collaboration between data producers and consumers to maintain quality.

🔍 Why It Matters

  • Consistency Unified features eliminate training-serving skew.
  • Platform-managed transformations ensure identical semantics across use cases.
  • Governance Registries and policies control who can promote models.
  • Automated gates enforce testing, fairness, and risk assessments before production.
  • Scalability Shared infrastructure supports hundreds of concurrent services.
  • Multi-tenant isolation prevents noisy neighbor issues and meets regulatory requirements.
  • Agility Automation reduces the time from insight to production update.
  • Metadata-driven triggers accelerate retraining when business conditions change.
  • Strategic alignment Platform analytics connect model performance to business outcomes, guiding investment.

🧰 Tools & Frameworks

Tool Purpose
Feast Feature store managing batch and online retrieval.
Tecton Enterprise feature platform with governance.
MLflow Registry Track model versions, stages, and lineage.
Sagemaker Model Registry Managed lifecycle for AWS deployments.
Seldon Core Multi-model serving with traffic routing and A/B tests.
KServe Kubernetes-native serving with inference graphs and autoscaling.
Metaflow Human-friendly data science platform with orchestration.
DataHub Metadata platform capturing lineage and governance metadata.
Flyte Workflow and metadata orchestration for ML applications.
Kubeflow Pipelines End-to-end ML workflow management integrated with metadata tracking.

Tooling Guidance - Choose feature store solutions that support both streaming and batch ingestion. - Evaluate model registry integrations with CI/CD, experiment tracking, and deployment engines. - Adopt metadata platforms capable of capturing custom schemas and linking to governance tools. - Implement serving frameworks that natively support canary, AB, and shadow rollouts. - Prioritize tools with strong APIs, multi-language SDKs, and role-based access control.

🧱 Architecture / Workflow Diagram

flowchart LR
A[Raw Data] --> B[Feature Engineering Pipeline]
B --> C[Feature Store Offline]
C --> D[Feature Store Online]
C --> E[Model Training]
E --> F[Model Registry]
F --> G[Deployment Orchestrator]
G --> H[Multi-Model Serving Gateway]
H --> TP["Traffic Policies (A/B, Shadow)"]
TP --> J[Monitoring & Feedback]
J --> K[Metadata Store]
K --> L[Automation & Governance]
L --> B

Diagram Walkthrough - Raw Data to Feature Pipeline transforms, validates, and versions features. - Feature Store Offline/Online ensures parity between training datasets and low-latency online features. - Model Training leverages tracked experiments and pulls consistent features. - Model Registry holds models with stages, approvals, and lifecycle metadata. - Deployment Orchestrator integrates with CI/CD and serving frameworks to deploy models. - Multi-Model Serving Gateway routes requests to models via policies (shadow, AB, ensemble). - Traffic Policies adapt to experimentation strategies and allow safe feature launches. - Monitoring & Feedback collects inference metrics, business KPIs, and drift signals. - Metadata Store centralizes lineage, metrics, and events that drive automation. - Automation & Governance trigger retraining, rollback, or compliance workflows based on metadata rules.

⚙️ Example Commands / Steps

from feast import FeatureStore
from mlflow.tracking import MlflowClient

store = FeatureStore(repo_path="feature_repo")
training_df = store.get_historical_features(
    entity_df=entity_df,
    features=["transactions:avg_spend_30d", "transactions:txn_count_7d"],
).to_df()
store.materialize_incremental(end_date=datetime.utcnow())

client = MlflowClient()
latest_prod = client.get_latest_versions("rideshare-eta", stages=["Production"])[0]
client.transition_model_version_stage(
    name="rideshare-eta",
    version=int(latest_prod.version)+1,
    stage="Staging",
)

Automation Workflow Snippet

# prefect deployment for automated retraining
name: retrain-eta-model
schedule:
  interval: 604800  # weekly
parameters:
  retrain_trigger: metadata.get("psi_eta", default=0)
flow_name: eta_pipeline
tags: ["auto-retrain", "rideshare"]

📊 Example Scenario

A ride-sharing platform serves dozens of micro-models—from ETA forecasting to surge pricing. Feast keeps feature parity, MLflow Registry enforces review gates, and Seldon Core manages shadow deployments before traffic is shifted to new versions. Metadata from DataHub tracks lineage across datasets, features, models, and dashboards. Automated policies trigger retraining when demand patterns shift dramatically due to events or weather.

Extended Scenario Narrative

  • Feature Engineering Team Publishes a new dynamic pricing feature, tagging ownership and quality metrics.
  • Model Team References the feature via Feast, runs experiments, and logs runs to MLflow with metadata linking to DataHub.
  • Governance Review Security and compliance officers approve promotion after fairness and explainability reports pass.
  • Deployment Seldon Core deploys the model in shadow mode; traffic policies collect feedback without impacting riders.
  • Promotion After metrics stabilize, traffic shifts gradually to the new model with continuous monitoring.
  • Lifecycle Management Model registry automatically archives older versions and notifies stakeholders.

Additional Use Cases

  • Financial Risk Platforms Manage hundreds of credit, fraud, and AML models with strict policy requirements.
  • Media Streaming Services Orchestrate personalization models across content, ads, and recommendation surfaces.
  • Supply Chain Optimization Run global simulations, scenario analyses, and automated retraining for demand shifts.
  • Healthcare Decision Support Govern explainable AI pipelines with audit trails and clinician oversight.
  • Retail Omnichannel Standardize features and models across store, web, and marketing touchpoints.

💡 Best Practices

  • ✅ Standardize metadata Use schemas for features, models, and datasets.
  • Adopt open standards (OpenLineage, ML Metadata) to ensure interoperability.
  • ✅ Automate policies Trigger retraining or deprecation via metrics thresholds.
  • Integrate business metrics (conversion, revenue) with technical metrics (drift, latency).
  • ✅ Enable multi-tenancy Isolate resources per product domain.
  • Utilize namespace separation, quotas, and billing tags.
  • ✅ Invest in tooling Provide self-service portals and SDKs for platform users.
  • Offer templates and wizards to reduce duplication and errors.
  • ✅ Foster platform governance councils Align stakeholders on priorities, standards, and roadmap.
  • ✅ Continuous education Run internal workshops on platform capabilities and policies.

⚠️ Common Pitfalls

  • 🚫 Feature sprawl Duplicated logic across teams increases technical debt.
  • Implement discovery portals and assign feature ownership.
  • 🚫 Registry bypass Shipping models without promotion workflows breaks auditability.
  • Enforce release gates integrating CI/CD, registry, and deployment approvals.
  • 🚫 Single cluster Hosting everything in one environment risks cascading failures.
  • Architect for multi-cluster deployments with failover.
  • 🚫 Ungoverned automation Automated retraining without guardrails can propagate issues.
  • Require human review for high-risk changes or deploy in shadow first.
  • 🚫 Opaque metadata Missing context limits trust and reuse.
  • Document lineage, assumptions, and owners for all artifacts.
  • 🚫 Ignoring cost Large feature stores and model fleets can balloon expenses.
  • Monitor storage and compute usage, set budget alerts, and retire unused assets.

🧩 Related Topics

  • Previous Topic 08_Cloud_and_Infrastructure.md
  • Establishes infrastructure foundations that support advanced capabilities.
  • Next Topic 10_End_to_End_Project.md
  • Demonstrates how advanced MLOps pieces combine in a comprehensive project.

🧭 Quick Recap

Step Purpose
Centralize features Ensure consistent online/offline data.
Govern models Control promotion, rollback, and lifecycle.
Scale serving Route traffic across many models safely.
Automate workflows Trigger retraining, rollback, and reporting based on metadata.
Empower teams Provide platforms and tooling for self-service innovation.

How to Apply This Recap - Use during platform planning to align on priority capabilities. - Review with governance boards to confirm policy coverage. - Share with domain teams to highlight platform services available to them.

🖼️ Assets

  • Diagram Architecture
  • Storyboard Idea Depict data flowing from feature creation to automated deployment and monitoring.
  • Dashboard Tip Showcase lineage graphs linking features, models, and business KPIs.

📘 References

  • Official docs https://docs.feast.dev/
  • Official docs https://www.seldon.io/solutions/mlops/
  • Official docs https://mlflow.org/docs/latest/model-registry.html
  • Blog posts https://mlops.community/advanced-mlops-platform-patterns
  • Blog posts https://medium.com/data-for-ai/platform-thinking-for-mlops
  • Blog posts https://cloud.google.com/blog/topics/ai-machine-learning/ml-platform-architecture
  • GitHub examples https://github.com/feast-dev/feast/tree/master/examples
  • GitHub examples https://github.com/SeldonIO/seldon-core/tree/master/examples
  • GitHub examples https://github.com/lsst-dm/registry