ML System Design Infrastructure - Roadmap

Machine learning infrastructure forms the backbone of reliable, scalable AI systems.
You’ll be evaluated on how well you connect data, models, and deployment pipelines, and how you design systems that scale gracefully while maintaining reproducibility.

⚙️ 1. Foundations of ML Infrastructure

1.1: Understand the End-to-End ML Lifecycle

1. Study the iterative nature of ML systems — Data → Feature Engineering → Training → Deployment → Monitoring → Feedback.
2. Understand the concept of drift — both data drift and concept drift — and how it impacts retraining frequency.
3. Map components to infrastructure:
   • Data Lake → Feature Store
   • Experiment Tracker → Model Registry
   • CI/CD → Model Deployment
   • Monitoring → Model Governance

Deeper Insight: Expect a question like, “If your model accuracy drops after deployment, where do you start debugging?” — You should discuss data versioning, feature consistency, and monitoring signals.

1.2: Learn the Infrastructure Stack Layers

1. Compute layer (GPUs, Kubernetes, distributed training systems like Ray or SageMaker).
2. Storage layer (object storage for datasets, model artifacts, logs).
3. Workflow orchestration (Airflow, Kubeflow, MLflow, Metaflow).
4. Observability layer (Prometheus, Grafana, Sentry, model performance dashboards).

Probing Question: “If you had to design a reproducible ML experiment pipeline, which layer is the most critical to get right first — and why?”

🧱 2. Model Registry and Experiment Tracking

2.1: Understand Model Versioning

1. Learn how to version models, parameters, and metrics.
   Tools: MLflow, Weights & Biases, or custom registries.
2. Understand semantic versioning (e.g., 1.2.0 → major/minor/patch updates).
3. Discuss model lineage — what data, code, and hyperparameters produced each model.

Probing Question: “Your model performance regresses. How do you trace it back to the dataset version or hyperparameter that caused it?”

2.2: Build a Model Registry Conceptually

1. Study the components:
   • Model metadata store (schema: model_name, version, metrics, artifact_path).
   • Approval workflows (staging → production).
   • Rollback and deprecation mechanisms.
2. Implement a simple registry with MLflow and SQLite for practice.

Deeper Insight: Discuss trade-offs between centralized and distributed registries — central provides consistency, distributed enables autonomy for multi-team environments.

🔁 3. CI/CD for Machine Learning

3.1: Understand the Differences Between ML CI/CD and Software CI/CD

1. In traditional CI/CD: Code → Build → Test → Deploy.
   In ML CI/CD: Data + Code + Model → Train → Validate → Register → Deploy.
2. Learn pipeline triggers:
   • New data arrival
   • Performance degradation
   • Manual approval for promotion

Probing Question: “Why can’t we deploy ML models with the same CI/CD pipeline used for backend services?”

3.2: Build an ML Deployment Pipeline

1. Use GitHub Actions or Jenkins to automate:
   • Data validation
   • Model training
   • Evaluation thresholds
   • Deployment to staging and production
2. Integrate MLflow or Sagemaker for model promotion.

Deeper Insight: Explain shadow deployment and canary release strategies for safe rollout. Be able to describe how you’d measure success before full rollout.

🧩 4. Feature Store and Data Consistency

4.1: Core Concepts of Feature Store

1. Learn about offline store (training data) and online store (serving data).
2. Understand how a feature store ensures consistent transformations between both.
3. Study point-in-time correctness to prevent data leakage.

Probing Question: “What is point-in-time correctness and how does it differ from a simple join on timestamp?”

4.2: Design a Minimal Feature Store

1. Model a simple schema: feature_name, entity_id, timestamp, value.
2. Implement a mini version using Feast or even PostgreSQL.
3. Discuss feature versioning and backfill strategies.

Deeper Insight: Be prepared to compare data warehouse-based vs. real-time feature stores in terms of latency and cost.

🧠 5. Workflow Orchestration and Automation

5.1: Orchestrate ML Pipelines

1. Study Airflow, Kubeflow Pipelines, or Metaflow.
   Learn about DAGs, operators, sensors, and backfilling.
2. Understand idempotency and checkpointing for resilient pipelines.
3. Learn retry policies and data dependency handling.

Probing Question: “If your model training step fails mid-way, how do you ensure the pipeline can recover without retraining everything?”

5.2: Build a Simple DAG-Based Workflow

1. Create an Airflow DAG:
   • Task 1: Data Validation
   • Task 2: Feature Engineering
   • Task 3: Model Training
   • Task 4: Model Evaluation and Registration
   • Task 5: Deployment
2. Integrate model metadata logging and artifact storage.

Deeper Insight: Discuss trade-offs between push-based (Airflow) and pull-based (Kubeflow) orchestration models.

📊 6. Monitoring, Logging, and Model Governance

6.1: Model Performance Monitoring

1. Track model-level metrics (accuracy, precision, recall, F1).
2. Track data-level metrics (distribution shifts, missing value ratio).
3. Use monitoring tools like Evidently AI, Prometheus, or Grafana.

Probing Question: “How would you know your model silently degraded in production without access to ground truth?”

6.2: Build Governance Workflows

1. Define governance policies — model approval, retention, audit logs.
2. Understand model cards and data lineage tracking.
3. Study compliance frameworks (GDPR, AI Act).

Deeper Insight: Explain how governance differs between regulated (finance, healthcare) and unregulated domains.

🚀 7. Scaling and Cost Optimization

7.1: Scaling Training and Serving

1. Study distributed training (Data Parallelism, Model Parallelism).
2. Learn serving architectures:
   • Batch inference
   • Online inference
   • Streaming inference
3. Implement autoscaling using Kubernetes Horizontal Pod Autoscaler.

Probing Question: “How would you reduce inference latency without retraining your model?”

7.2: Cost Optimization

1. Use spot instances or serverless compute for ephemeral workloads.
2. Cache embeddings or intermediate computations.
3. Design data pipelines with lazy evaluation to reduce cost.

Deeper Insight: Discuss trade-offs between latency (e.g., GPUs always on) and cost efficiency (on-demand or cold start).

🧩 8. Infrastructure as Code (IaC) and Security

8.1: Infrastructure as Code

1. Learn Terraform or AWS CloudFormation basics.
2. Automate resource provisioning (S3 buckets, EC2 clusters, EKS nodes).
3. Maintain environment parity across staging and production.

Probing Question: “How do you ensure reproducibility when retraining models across environments?”

8.2: Security and Access Control

1. Implement RBAC for model registries and feature stores.
2. Encrypt data at rest and in transit.
3. Audit API access for model endpoints.

Deeper Insight: Discuss least privilege principle and secret management in ML environments.