☑️ DSML: MLOps Lifecycle Checklist

☑️ DSML: MLOps Lifecycle Checklist

Plan → Data → Model → Deploy

🧭 A complete, validated, and easy-to-follow roadmap of the Machine Learning lifecycle — harmonized from Google, AWS, Microsoft, Deepchecks, and Neptune.ai frameworks.

MLOps Illustrated: MLOps = Data + ML + Dev + Sec + Ops

---

🧭 Phase 1: Plan — Planning & Scoping

Define the problem, align stakeholders, assess feasibility, and establish the project roadmap.

Icon	Checklist Item	Explanation
🎯	Business Objective Defined	Clarify what business outcome or decision the model supports. Define measurable KPIs (e.g., reduce churn by 10%).
👥	Stakeholders Mapped	Identify owners, contributors, reviewers, and end users for full accountability.
🧮	Baseline / Benchmark Established	Define a simple rule-based or non-ML baseline for measuring improvement.
📐	ML Task Framed	Translate business needs into ML paradigms (classification, regression, clustering, recommendation).
📊	Success Metrics Chosen	Pick both technical metrics (AUC, RMSE) and business metrics (ROI, revenue uplift).
🗂️	Data Availability & Quality Assessed	Inventory potential data sources, check for completeness, accessibility, and reliability.
🔗	Infrastructure & Tooling Decided	Select your ML stack (frameworks, pipelines, versioning, compute environment).
🗓️	Roadmap & Milestones Set	Define phase-wise deliverables, cost, and schedule using agile or Kanban tracking.
⚠️	Risk, Ethics & Governance Reviewed	Address privacy, fairness, and compliance (PII, GDPR). Document known risks and mitigations.
✅	Go/No-Go Gate Passed	Ensure alignment with stakeholders, feasibility confirmed, and approval obtained before proceeding.

---

🧪 Phase 2: Data — Preparation & Understanding

Acquire, prepare, explore, and validate data before modeling.

Icon	Checklist Item	Explanation
🚚	Data Ingestion Completed	Gather raw data via APIs, databases, logs, sensors, or files (batch/stream).
🔍	Data Profiling & Understanding Done	Inspect schemas, distributions, nulls, correlations, and summary stats.
🧼	Data Cleaning & Conditioning	Handle missing values, duplicates, outliers, incorrect data types, and unit inconsistencies.
📐	Data Transformation & Feature Engineering	Normalize, encode, scale, and generate new features (aggregations, ratios, embeddings).
🧮	Feature Selection & Dimensionality Reduction	Retain key features (filter/wrapper/embedded), apply PCA or UMAP where appropriate.
📊	Exploratory Data Analysis (EDA) Conducted	Visualize data relationships, trends, and anomalies; check for leakage or imbalance.
🏷️	Labeling / Annotation Completed	For supervised tasks: ensure accurate and consistent labels with quality checks.
📦	Data Validation & Storage Versioned	Validate schema and splits; store versioned datasets in a governed data lake/warehouse.
🔄	Data Observability & Drift Pipeline Defined	Plan continuous monitoring for data quality, freshness, and drift detection post-deployment.

---

🤖 Phase 3: Model — Building, Evaluation & Packaging

Build, evaluate, optimize, and prepare models for deployment with full traceability.

Icon	Checklist Item	Explanation
🔍	Algorithm Candidates Selected	Choose appropriate models based on data size, interpretability, and latency constraints.
⚙️	Training Pipeline Built & Tracked	Modularize code (fit, predict, evaluate); use experiment trackers (MLflow, W&B, Comet).
🔄	Model Training & Cross-Validation Executed	Train using k-fold, time-series, or stratified splits; evaluate over multiple seeds.
📉	Hyperparameter Tuning Performed	Optimize via grid/random/Bayesian search; use early stopping and parallelization.
📊	Model Evaluation & Diagnostics Completed	Compute metrics, check for bias, calibration, overfitting, and robustness to perturbations.
🎯	Model Selection & Champion Chosen	Compare performance, interpretability, and efficiency; register best candidate.
🛠️	Model Serialization & Packaging Done	Save model artifacts, preprocessing code, configs, and environment dependencies.
🧪	Pre-Deployment Testing Passed	Perform integration, latency, and reproducibility tests; shadow or A/B test if feasible.
📜	Documentation & Model Card Prepared	Summarize purpose, data used, performance, risks, and limitations per model card format.

---

🚀 Phase 4: Deploy — Productionization & Lifecycle Management

Move validated models into production and establish monitoring, retraining, and governance.

Icon	Checklist Item	Explanation
⚙️	Deployment Strategy Adopted	Choose mode: batch, online API, streaming, edge, or hybrid; define rollout plan.
🧱	CI/CD & IaC Pipelines Implemented	Automate testing, packaging, and deployment via GitHub Actions, Jenkins, or ArgoCD.
🖥️	Model Serving Layer Live	Deploy REST/gRPC API; validate request schemas, latency, and logging.
📈	Monitoring & Observability Activated	Track performance, latency, error rates, data drift, and business metrics.
🔁	Retraining Pipeline Implemented	Automate retraining triggers (schedule or drift-based) with version control and human review.
🛡️	Security & Governance Enforced	Manage access, encrypt data, log usage, enforce compliance and fairness checks.
🔧	Rollback & Fallback Mechanism Ready	Define safe fallback (baseline model or previous version) for automatic rollback.
📚	Documentation & Runbook Finalized	Include incident response, maintenance procedures, version notes, and contact matrix.
📊	Post-Deployment Review Conducted	Compare live KPIs vs. planned metrics; confirm value realization and feedback into planning.

---

🔄 Lifecycle Summary Flow

PLAN → DATA → MODEL → DEPLOY → (Monitoring/Feedback) → PLAN

• Iterative feedback loops occur especially between:
  • Model ↔ Data (feature drift, retraining)
  • Deploy ↔ Model (concept drift, monitoring insights)
  • Deploy ↔ Plan (business realignment)

---

⚙️ Canonical Jargon Reference

Term	Meaning
Feature Engineering	Creating or transforming data attributes to improve model learning.
Drift	Change in data or concept distributions over time affecting model accuracy.
Model Card	Standardized documentation summarizing model purpose, performance, and ethical considerations.
CI/CD	Continuous Integration / Continuous Deployment — automated testing and rollout pipelines.
IaC	Infrastructure as Code — declaratively managing resources for reproducible environments.

---

✅ Phase Exit Gates (for Governance)

Phase	Exit Criteria
Plan	Business goals, feasibility, risks, and success metrics approved.
Data	Clean, validated, versioned data ready for modeling.
Model	Champion model validated, reproducible, and documented.
Deploy	CI/CD automated, monitoring active, governance verified.

---

⚠️ Common Pitfalls & Remedies

Pitfall	Impact	Remedy
Unclear business objective	Misaligned outcomes	Write SMART goals and measurable KPIs
Unversioned data/models	Non-reproducible results	Use DVC/MLflow registry
Over-tuned model	Overfitting / poor generalization	Use cross-validation and baseline comparison
Manual deployment	High risk of errors	Automate CI/CD pipeline
No drift monitoring	Model silently degrades	Implement data + concept drift detection
Missing rollback	Unrecoverable failure	Use canary/blue-green strategy

---

🔗 Trusted References

• Google Cloud: MLOps Continuous Delivery Guide
• AWS SageMaker: Model Deployment & Monitoring
• Microsoft TDSP: Data Science Process
• Deepchecks: ML Lifecycle
• Neptune.ai: ML Project Lifecycle
• Analytics Vidhya: Machine Learning Lifecycle Explained

---

🧩 Key Insight

“Successful ML systems are not about the best model — they are about the best lifecycle.”
— Adapted from Google ML Engineering Playbook

---

⭐ Author’s Note:
This checklist is designed for DS/ML practitioners and educators seeking a clear, canonical lifecycle reference — ready for integration into MLOps pipelines, project templates, or portfolio documentation.