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EXPERT ROADMAP
MLOps: Deploy & Monitor ML in Production
MLOps: Deploy & Monitor ML in Production
Ship ML models that stay accurate, observable, and cost-efficient in production at scale.
CREATED BY
M
Manu V. ★ 5.0
Senior Software Engineer at SoftGiant | 16+ years of experience
About this Path
For ML engineers, data scientists, and platform engineers who already train models and need to close the gap between notebook and production. This roadmap covers the full MLOps lifecycle: CI/CD for ML, serving infrastructure, feature management, drift monitoring, and cost governance. You build a reference architecture on AWS or GCP using real open-source tooling and leave with a reusable template for your next production ML system.
Path Overview
Advanced LevelCertificate of CompletionAbout 68 hours to completeEnglish language20+ curated videosLearn online at your own pace6 modules with resourcesGamified & interactive
Path Curriculum
MLOps Maturity Levels: Where You Are, Where You Need to Be
Level 0 (notebooks) → Level 3 (automated retraining with online serving) explained with org context.
The Reference MLOps Architecture on AWS/GCP
Data store → feature store → training pipeline → model registry → serving → monitoring layer.
Choosing Your Stack: SageMaker vs. Vertex AI vs. DIY Kubernetes
Trade-off matrix for managed services vs. open-source; total cost and team skill implications.
Versioning Data, Code, and Models Together with DVC and MLflow
Reproducible experiments where every model can be traced back to exact code, data, and params.
ML-Specific Tests: Data Validation, Training Smoke, Inference Contract
Great Expectations for data; training smoke tests; schema contracts between training and serving.
GitHub Actions Pipelines for Model Training and Deployment
Trigger retraining on data change, run evaluation gates, and push to registry on pass.
Model Registry Promotion Workflows with MLflow
Staging → production promotion with automated evaluation thresholds and approval gates.
Blue-Green and Canary Deployments for ML Models
Route 5% traffic to a new model, compare business KPIs for 24 hours, then promote.
Feature Store Architecture: Offline, Online, and the Consistency Problem
Why training-serving skew happens and how a feature store's materialization layer prevents it.
Building and Serving Features with Feast
Define feature views, materialize to Redis, serve point-in-time correct features in training jobs.
Real-Time Feature Pipelines with Kafka and Flink
Stream events, compute aggregate features, and push to the online store under 10ms.
Feature Lineage and Discovery
Tag, document, and search features so teams reuse rather than recompute the same signals.
Serving Patterns: Online, Batch, Streaming, and Embedded
Match serving pattern to latency SLO and throughput; never over-engineer real-time for a batch use case.
KServe on Kubernetes: InferenceService, GPU Autoscaling, Rollouts
Deploy a PyTorch model with GPU node affinity, HPA on custom metrics, and zero-downtime updates.
Optimizing Inference Latency: Quantization, Batching, Caching
INT8 quantization with ONNX Runtime; dynamic batching in Triton; semantic cache with Redis.
Serving LLMs in Production: vLLM, TGI, and Rate Limiting
Deploy a 7B parameter model with vLLM's paged attention; enforce per-tenant token-rate limits.
Types of Drift: Data, Concept, and Prediction Drift
Statistical tests (PSI, KS, chi-squared) and which one is appropriate for each feature type.
Evidently AI Dashboards and Automated Retraining Triggers
Configure drift thresholds, emit alerts to PagerDuty, and trigger Airflow DAGs on breach.
Business Metric Monitoring: Closing the Feedback Loop
Log model decisions and ground truth labels; compute precision/recall weekly without a data scientist.
Structured Logging and Distributed Tracing for ML Inference
Correlate a prediction ID through feature retrieval, model inference, and downstream business events.
ML Infrastructure Cost Profiling
Break down training, storage, and serving costs; identify the top three cost drivers in any ML system.
Spot and Preemptible Instances for Training Workloads
Checkpoint-and-resume patterns that make 70% training cost reduction safe and operationally sound.
Multi-Tenancy and Resource Quotas on Kubernetes
Namespace-level resource quotas, priority classes, and fair-share scheduling for shared ML clusters.
Incident Response Runbooks for ML Systems
Structured runbooks for the five most common ML production incidents; on-call rotation design.
What you'll learn
- ✓Design and implement a CI/CD pipeline for ML that runs tests, retrains on schedule, and promotes models through staging to production without manual intervention.
- ✓Build and operate a feature store using Feast or Tecton that eliminates training-serving skew across batch and real-time pipelines.
- ✓Deploy inference services on Kubernetes with KServe or Seldon, configure GPU autoscaling, and enforce p99 latency SLOs in production.
- ✓Detect data drift, concept drift, and prediction degradation using Evidently AI and route traffic to fallback models automatically.
- ✓Instrument ML systems with structured logging, distributed tracing, and custom Prometheus metrics surfaced in Grafana dashboards.
- ✓Reduce ML infrastructure cost by 30-50% using spot instances, model quantization, request batching, and intelligent caching strategies.