30 Days of MLOps Challenge · Day 1

Intro to MLOps iconIntro to MLOps – ML Meets DevOps

By Aviraj Kawade · June 3, 2025 · 5 min read

MLOps extends DevOps to the ML lifecycle so teams can automate, reproduce, and scale model development, deployment, and monitoring.

Welcome

Hey — I'm Aviraj 👋

Understanding MLOps equips engineers to handle the unique challenges of deploying ML models—like data drift, versioning, and retraining—which traditional DevOps doesn't fully address. It enables automation, reproducibility, and scalability for ML workflows, making AI systems production‑ready and maintainable.

Watch: Intro to MLOps

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MLOps Pipeline Flow

MLOps Pipeline Flow Vertical flow: Data Ingestion → Data Validation → Feature Store → Data Preprocessing → Model Training → Model Evaluation → Model Validation (decision) → Model Registry → Model Packaging → Deployment Staging → A/B or Canary Test (decision) → Production Deployment → Prediction Service → Monitoring & Logging → Trigger Retraining feedback to start. Side lane shows Data Sources. Data Sources Databases Data Lake Data Ingestion Data Validation Feature Store Data Preprocessing Model Training Model Evaluation Model Validation Model Registry Model Packaging Deployment Staging A/B or Canary Test Production Deployment Prediction Service Monitoring & Logging Revise Approved Fail → Pass Trigger Retraining

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Key Learnings

  • What MLOps is and how it extends DevOps to the ML lifecycle.
  • Top challenges of deploying and maintaining ML models in production.
  • Key benefits of MLOps across automation, reproducibility, and governance.
  • Overview of the ML lifecycle and how CI/CD/CT differs for ML.
  • Comparison between traditional software CI/CD and ML CI/CD pipelines.
  • Learn here: video + this page.

What is MLOps?

MLOps (Machine Learning Operations) combines machine learning with DevOps practices to streamline the end‑to‑end ML lifecycle — from data preparation to model deployment and monitoring.

How MLOps Extends DevOps

AspectDevOps FocusMLOps Extension
CodeVersion control, CI/CD for app codeVersion control for code and ML models
Build & TestUnit/integration testsData & model validation, reproducibility tests
DeploymentAutomated app deploymentsAutomated model deployment, versioning, rollback
MonitoringApp performance, uptime, logsModel/data drift, inference performance
CollaborationDev & OpsData Scientists, ML Engineers, DevOps

Key Concepts

  • ML Lifecycle Management: data → features → train → validate → deploy → monitor
  • Model Reproducibility: same code/data yields same artifacts
  • Continuous Training (CT) with triggers from drift or schedules
  • Model & Data Versioning and lineage
  • CI/CD/CT Pipelines purpose-built for ML

Challenges of Production ML

  1. Data & Concept Drift — changing inputs/relationships degrade accuracy.
  2. Versioning & Reproducibility — track code, data, configs across envs.
  3. CI/CD for ML — automate training, testing, packaging, deploy.
  4. Scalability & Performance — low-latency inference at scale.
  5. Monitoring & Observability — metrics, alerts for model quality.
  6. Security & Governance — access control, PII, compliance.
  7. Retraining & Continuous Learning — scheduled or triggered CT.
  8. Dependency Management — consistent runtime via containers.
  9. Cross‑functional Collaboration — align DS/ML/DevOps/Product.
  10. Explainability & Bias — transparent, fair predictions.

Key Benefits of MLOps

  • Automation of ML workflows (CI/CD for models).
  • Reproducibility across experiments and environments.
  • Monitoring for drift and performance degradation.
  • Governance via lineage and audit trails.
  • Collaboration with standardized workflows.
  • Scalability using Kubernetes/cloud/serverless.
  • Experiment Tracking with params/metrics/artifacts.
  • Continuous Training & Deployment to keep models fresh.

ML Lifecycle

  1. Data Collection
    • Gather raw data from logs, sensors, databases, and APIs.
    • Ensure diversity and representativeness to avoid bias.
    • Fix quality issues: missing values, duplicates, outliers.
  2. Training
    • Prepare datasets (cleaning, transformations, feature engineering).
    • Select appropriate algorithms and training strategies.
    • Tune hyperparameters for optimal performance.
  3. Validation
    • Evaluate on validation sets with metrics (accuracy, precision, recall, F1, ROC‑AUC).
    • Use cross‑validation to assess robustness and variance.
    • Detect and mitigate overfitting/underfitting.
  4. Deployment
    • Package and serve the model (APIs, batch jobs, streaming).
    • Use containers/serverless for portability and scale.
    • Integrate with CI/CD for automated, reproducible releases with versioning.
  5. Monitoring
    • Track latency, throughput, and infrastructure health.
    • Monitor model/data drift and performance degradation over time.
    • Set up alerting and automated retraining (CT) pipelines.

Traditional CI/CD vs ML CI/CD

Feature/StageTraditional CI/CDML CI/CD
Code SourceApplication codeCode + Data + Model
Version ControlSource in GitCode, datasets, models, experiments
Build PhaseCompile/packagePrep data, train, produce model artifact
Test PhaseUnit/integration/E2EData validation, model eval, bias checks
ArtifactBinaries, containersModels, reports, metadata
Deploy TargetServers/containers/FaaSModel registry, inference services
MonitoringLogs, metrics, SLOsModel/data drift, accuracy
RollbackRedeploy previous versionRedeploy or retrain previous model
TriggersCode pushes, PRsCode, data drift, metric degradation
FrameworksJenkins, ActionsMLflow, Kubeflow, TFX, SageMaker
Reproducibility FocusEnvironment consistencyData, model, environment, and metric consistency
CollaborationDevelopers + OpsData Scientists, ML Engineers, DevOps

How to Participate

  • Complete the tasks and challenges above.
  • Document your progress and key takeaways on GitHub README, Medium, or Hashnode.
  • Share a LinkedIn/X post tagging Aviraj Kawade and use #MLOps and #60DaysOfDevOps.

Challenges to Try

  • Summarize the difference between DevOps and MLOps in your own words.
  • List 5 real‑world problems that MLOps helps solve (e.g., model drift, reproducibility).
  • Sketch a basic ML lifecycle and mark where MLOps adds value.
  • Post a reflection: “Why DevOps skills are important for ML Engineers” on LinkedIn/X.
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