DevOps to MLOps

The Essential Tools and Concepts You Must Master

MLOps is not “data science with YAML” — it’s distributed systems, reliability, and cost control for models.

Below is a clean mental map of what actually matters when a DevOps → MLOps transition is done correctly.

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Core Concept Shift (Most Important)

DevOps mindset

Deploy services reliably

MLOps mindset

Deploy uncertainty reliably

Models:

• Change behavior without code changes

• Degrade silently

• Cost money per inference

• Depend on data quality, not just uptime

If you internalize this, everything else clicks.

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2️⃣ Model Lifecycle (You must own this end-to-end)

You should be comfortable with:

1. Data ingestion

2. Feature engineering

3. Training

4. Validation

5. Packaging

6. Serving

7. Monitoring

8. Retraining

DevOps engineers usually jump straight to serving.
Strong MLOps engineers control the full loop.

Data Fundamentals (Non-Negotiable)

You don’t need to be a data scientist, but you must understand:

Concepts

• Training vs validation vs test sets

• Batch vs streaming data

• Data leakage (silent model killer)

• Feature skew (train ≠ inference data)

• Dataset versioning

Tools

• S3
  → Acts as the source of truth for raw, processed, and training data

• Delta Lake / Iceberg / Hudi
  → Enables reproducible datasets with versioning, schema evolution, and time travel

• Kafka
  → Powers real-time data streams and online feature generation

• Airflow / Dagster / Prefect
  → Orchestrates data pipelines, model training, and retraining workflows

💡 If you understand Airflow + data contracts, you’re already ahead.

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Feature Stores (The Missing Layer)

Why feature stores exist

• Prevent training/serving skew

• Reuse features across teams

• Enable low-latency inference

Tools

• Feast (industry standard)

• Tecton (enterprise)

• Vertex AI Feature Store

👉 Think of a feature store as:

A ConfigMap for models, but with time awareness

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Training Infrastructure

What changes from DevOps

• Training is ephemeral

• Failures are expected

• Cost is the main enemy

Key concepts

• Distributed training

• Spot/preemptible instances

• Checkpointing

• GPU scheduling & bin packing

Tools

• Kubernetes
  → Serves as the control plane for training and inference workloads

• Kubeflow / Argo Workflows
  → Orchestrates model training pipelines and ML workflows

• Ray
  → Enables distributed machine learning and parallel model training

• Spark
  → Handles large-scale feature processing and data transformations

• NVIDIA Operator
  → Manages the GPU lifecycle, drivers, and device plugins on Kubernetes

💡 GPU utilization matters more than CPU ever did.

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6️⃣ Model Packaging & Registry

Must-know ideas

• Model ≠ code

• Model version ≠ data version

• Rollback must be instant

Tools

• MLflow (mandatory knowledge)

• Weights & Biases

• Hugging Face Hub (LLMs)

Think of MLflow as:

Docker Registry + Git + Metrics for models

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Model Serving (This is where DevOps shines)

Serving patterns

• Batch inference

• Online REST/gRPC

• Streaming inference

• Async inference queues

Tools

• KServe
  → Used for general ML model serving on Kubernetes

• Triton Inference Server
  → Optimized for high-performance GPU inference

• vLLM
  → Designed specifically for LLM inference with efficient memory usage

• BentoML
  → Enables fast model productization and API packaging

• FastAPI
  → Ideal for custom model serving and bespoke inference logic

👉 This is where your Kubernetes + ingress skills dominate.

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Observability for Models (Different from apps)

What to monitor

• Prediction distribution drift

• Feature drift

• Model accuracy decay

• Latency per token (LLMs)

• Cost per request

Tools

• Evidently AI

• WhyLabs

• Prometheus + custom metrics

• OpenTelemetry (for inference traces)

Models can be “up” and still be useless.

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CI/CD for ML (Not GitOps alone)

Differences from DevOps CI/CD

• Pipelines trigger on data

• Artifacts are models

• Rollouts depend on metrics

Patterns

• Shadow deployments

• Canary inference

• A/B testing

• Human-in-the-loop approvals

Tools

• GitHub Actions / GitLab CI

• Argo CD (for serving)

• Kubeflow Pipelines (training)

• Feature flags for models

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Cost Engineering (Your secret weapon)

This is where most ML teams fail.

You should understand:

• GPU time vs batch size

• Token-based pricing

• Model quantization

• Caching embeddings

• Autoscaling pitfalls for GPUs

💡 An MLOps engineer who saves GPU cost is more valuable than one who trains a better model.

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Learning Order (Practical Path)

Since you already have strong infra skills, I’d suggest:

1. MLflow + experiment tracking

2. Feature stores (Feast)

3. Model serving (vLLM / Triton)

4. Drift monitoring

5. Cost optimization for GPUs

6. Full lifecycle automation

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Final Reality Check

MLOps is DevOps + Data + Statistics + Cost Engineering

You don’t need to become a data scientist.
You need to become the person who makes models reliable, cheap, and trustworthy.

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