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MLOps roadmap

MLOps applies DevOps practices to machine learning: versioned data and models, automated training pipelines, safe deployment and monitoring for drift. This roadmap walks the full MLOps lifecycle, from data to retraining.

Muda
⏱ 3–4 months
Level
Intermediate
Steps
6 steps
Architecture ↓Cheat sheet ↓4 interview Q&As ↓
Shiriki

The roadmap

Click a step to open the full guide.

Best architecture: the MLOps lifecycle and platform

A closed loop: every model in production is traceable to its data and code, monitored, and retrained automatically.

MLOps lifecycle

  1. Data (versioned, validated)
  2. Train (tracked experiments)
  3. Evaluate & register
  4. Deploy (canary / batch)
  5. Monitor (drift, accuracy)
  6. Retrain

Repeats as a loop

Why it's built this way

  • Feature store + data validation keep training and serving consistent.
  • Experiment tracking + model registry make every model reproducible.
  • CI/CD runs tests and only promotes models that beat the current one.
  • Monitoring watches drift and accuracy, and triggers continuous training.

MLOps cheat sheet

The essentials on one screen. Bookmark it for revision.

Lifecycle at a glance

DataVersion, validate, shared features
TrainTracked experiments, reproducible env
EvaluateHold-out test, beat the champion
RegisterApproved versions with stages
DeployBatch or API, shadow/canary
MonitorService health, drift, accuracy
RetrainScheduled or drift-triggered

Improve performance

  • Prefer batch inference where real-time isn't required.
  • Quantise or distil models for faster, cheaper serving.
  • Cache features and frequent predictions.
  • Autoscale serving on traffic; batch requests on GPUs.

Security checklist

  • Access control on data, models and the registry.
  • Sign and scan model artefacts; only deploy from the registry.
  • Protect PII in training data; log access for audits.
  • Watch for data poisoning in pipelines fed by external data.

Most important interview questions

Click a question to see a strong answer. Each step also has its own questions.

1Explain the MLOps lifecycle.

Data collection and validation, feature engineering, training with experiment tracking, evaluation, registration, deployment, monitoring for drift and performance, and retraining, all automated and versioned.

2Data drift vs concept drift?

Data drift: input distributions change. Concept drift: the relationship between inputs and the target changes, so the same inputs should now give different outputs.

3What tools make up an MLOps stack?

Data versioning/validation, a feature store, experiment tracking, a model registry, a pipeline orchestrator, a serving layer and monitoring; often on Kubernetes or managed cloud ML services.

4How do you make ML experiments reproducible?

Version code, data and environment, fix random seeds, and log all parameters and artefacts for every run.

Ramani Related

6

Machine Learning Engineer

6 steps · 6–9 months · Advanced

Train, deploy and monitor machine learning models.

View roadmap →
6

LLMOps

6 steps · 2–3 months · Intermediate

Run LLM applications in production: prompts, evals, cost and safety.

View roadmap →
5

Docker & Kubernetes

5 steps · 4–6 weeks · Intermediate

Package apps and run them anywhere.

View roadmap →
6

Data Scientist

6 steps · 6–8 months · Intermediate

Use statistics and machine learning to predict and explain.

View roadmap →
1

MLOps · Step 1 of 6 · 1 week

The MLOps lifecycle and maturity levels

See the whole loop before automating it.

A model isn't finished when it's trained. Data changes, the world changes, and accuracy quietly drops. MLOps turns the one-off notebook into a repeatable loop: data, training, evaluation, deployment, monitoring and retraining.

Teams usually mature from manual work (level 0), to automated training pipelines (level 1), to fully automated CI/CD for models (level 2).

What you'll learn

  • Lifecycle stages
  • Manual vs automated ML
  • MLOps maturity levels
  • Roles: data scientist, ML engineer, platform
  • Why models decay

MLOps maturity

Level 0

  • Manual notebooks
  • Hand-deployed models

Level 1

  • Automated training pipeline
  • Continuous training

Level 2

  • CI/CD for pipelines
  • Automated testing & deployment

Key concepts

MLOps
DevOps practices applied to machine learning models and data.
Model decay
Accuracy dropping over time as real-world data changes.
Maturity level
How automated your ML lifecycle is (0 = manual, 2 = full CI/CD).

Step by step

  1. 1.1

    Map the loop

    Data → train → evaluate → deploy → monitor → retrain.

  2. 1.2

    Find manual steps

    Every copy-paste or notebook rerun is an automation target.

  3. 1.3

    Pick a maturity target

    Level 1 (automated training) is a great first goal.

Notes

  • Most ML failures in production are data problems, not algorithm problems.

Tips & tricks

  • Draw your current workflow and mark every manual step; those are your automation targets.

Interview questions

MLOps and why is it needed ni nini?

MLOps is the set of practices for reliably building, deploying and maintaining ML models in production. It's needed because models depend on changing data, so they must be versioned, tested, monitored and retrained.

The quick way to study this

Learn the six lifecycle stages and the three maturity levels.

2

MLOps · Step 2 of 6 · 2 weeks

Data versioning and validation

Reproducible, trustworthy training data.

You can't reproduce a model if you can't reproduce its data. Version datasets alongside code, validate every new batch against expectations, and compute features in one shared pipeline used by both training and serving.

What you'll learn

  • Dataset versioning
  • Data validation (schemas, ranges)
  • Feature engineering pipelines
  • Maduka Feature
  • Data lineage

Data pipeline

  1. Raw sources
  2. Validate
  3. Transform features
  4. Version
  5. Feature store

Key concepts

Data drift
Input data distribution changes from training data.
Feature store
Central, consistent features for training and serving.

Step by step

  1. 2.1

    Version data

    Snapshot or hash each training dataset.

  2. 2.2

    Validate

    Schema, ranges, missing values, distribution checks.

  3. 2.3

    Share features

    One feature pipeline for training and serving.

Notes

  • Training–serving skew is a classic silent bug.

Tips & tricks

  • Write data tests like unit tests and fail the pipeline when they break.

Interview questions

How do you prevent training–serving skew?

Compute features with the same code in both places (often a feature store), validate inputs at serving time and monitor feature distributions.

The quick way to study this

Version one dataset and add five validation checks.

3

MLOps · Step 3 of 6 · 2 weeks

Experiment tracking and model registry

Know exactly which model is which.

Every training run should record its code version, data version, parameters, metrics and the model file. A model registry then holds approved models with stages such as staging and production, so deployments always use a known model.

What you'll learn

  • Experiment tracking (params, metrics, artefacts)
  • Model registry and stages
  • Model cards
  • Reproducible environments

From run to production

  1. Training run
  2. Tracked metrics
  3. Linganisha
  4. Register
  5. Staging
  6. Production

Key concepts

Experiment tracking
A record of every training run and its results.
Model registry
A catalogue of approved model versions with stages.

Step by step

  1. 3.1

    Log every run

    Code commit, data version, params, metrics, artefacts.

  2. 3.2

    Compare runs

    Pick the best by agreed metrics.

  3. 3.3

    Register

    Promote approved models: staging → production.

Notes

  • If you can't answer 'which data trained the model in production?', you're not doing MLOps yet.

Tips & tricks

  • Write a short model card: purpose, data, metrics, limitations.

Interview questions

What should a model registry store?

Model artefacts and versions, their training data and code versions, metrics, stage (staging/production/archived), approver and model card.

The quick way to study this

Track ten experiments and register the best model.

4

MLOps · Step 4 of 6 · 3 weeks

Training pipelines and CI/CD for ML

Automate train, test and release.

Turn the notebook into a pipeline: ingest → validate → train → evaluate → register. Run it on a schedule or when new data arrives (continuous training), and gate promotion on evaluation results compared with the current model.

What you'll learn

  • Pipeline orchestration
  • Automated testing for ML
  • Continuous training
  • CI/CD for models
  • Infrastructure as code

ML CI/CD

  1. Code/data change
  2. Tests
  3. Train
  4. Evaluate vs champion
  5. Register
  6. Deploy

Key concepts

Continuous training
Retraining automatically when data or schedule says so.
Champion vs challenger
Current production model vs a new candidate.

Step by step

  1. 4.1

    Pipeline

    Ingest → validate → train → evaluate → register as code.

  2. 4.2

    Triggers

    Schedule, new data or drift alerts start retraining.

  3. 4.3

    Gates

    Promote only if the new model beats the champion.

Notes

  • Only promote a model that beats the current one on the same test set.

Tips & tricks

  • Start with a simple scheduler before adopting a full orchestration platform.

Interview questions

How is CI/CD for ML different from normal software?

It tests data and models as well as code, retrains on new data, and promotes based on evaluation metrics, not just passing unit tests.

The quick way to study this

Build a pipeline that retrains and evaluates automatically.

5

MLOps · Step 5 of 6 · 2 weeks

Deployment and serving

Batch, real-time and safe rollouts.

Choose batch predictions when you can (cheaper and simpler) and online APIs when you must. Roll out new models gradually with shadow or canary deployments and keep the previous model ready for instant rollback.

What you'll learn

  • Batch vs online inference
  • Model APIs and containers
  • Shadow, canary and A/B deployments
  • Rollbacks
  • Scaling inference

Batch vs online inference

Batch

  • Scheduled jobs
  • Cheap, simple
  • Daily scores, reports

Online

  • Real-time API
  • Low latency needed
  • Fraud checks, recommendations

Key concepts

Shadow deployment
New model runs on live traffic but its answers aren't used.
Canary
Send a small share of traffic to the new model first.

Step by step

  1. 5.1

    Choose serving mode

    Batch jobs or real-time API.

  2. 5.2

    Containerise

    Model + preprocessing in one image.

  3. 5.3

    Roll out safely

    Shadow → canary → full; keep rollback ready.

Notes

  • Package preprocessing with the model so they never drift apart.

Tips & tricks

  • Shadow-deploy: run the new model on live traffic without using its answers, then compare.

Interview questions

How would you safely replace a production model?

Shadow-deploy the challenger, compare its predictions and metrics, then canary a small share of traffic, monitor, and roll forward or roll back instantly to the champion.

The quick way to study this

Containerise a model API and practise a rollback.

Docker & Kubernetes roadmap →
6

MLOps · Step 6 of 6 · 2 weeks

Monitoring, drift and governance

Catch problems before users do.

Monitor two things: the service (is it up and fast?) and the model (are inputs and predictions still like the training data, and is accuracy holding?). Trigger alerts or retraining when drift crosses thresholds, and keep an audit trail for regulated uses.

What you'll learn

  • Service metrics (latency, errors)
  • Data and concept drift
  • Performance monitoring with delayed labels
  • Alerting and retraining triggers
  • Fairness, audit and compliance

What to monitor

  1. Business KPIs
  2. Model accuracy (delayed labels)
  3. Prediction drift
  4. Input/feature drift
  5. Service health

Key concepts

Concept drift
The relationship between inputs and outcomes changes.
PSI / KS test
Common statistics for measuring distribution drift.

Step by step

  1. 6.1

    Service metrics

    Latency, errors, throughput.

  2. 6.2

    Model metrics

    Input drift, prediction drift, accuracy when labels arrive.

  3. 6.3

    Act

    Alerts, retraining triggers, audit logs.

Notes

  • Labels often arrive late; watch input and prediction drift as early warnings.

Tips & tricks

  • Build one dashboard with traffic, latency, drift and accuracy side by side.

Interview questions

How do you monitor a model when true labels arrive weeks later?

Watch input and prediction drift as early warnings, track proxy metrics, and compute real accuracy when labels arrive.

The quick way to study this

Add a drift check and an alert to your deployed model.

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