From DevOps to MLOps

Introduction: Why MLOps Matters Now More Than Ever

As a DevOps engineer, I’ve always been fascinated by systems that run at scale — whether that’s an infrastructure serving millions of users or CI/CD pipelines keeping deployments flowing. But recently, a new operational challenge has emerged at the intersection of data science, software engineering, and infrastructure: Machine Learning Operations (MLOps).

With AI reshaping industries across the board — from healthcare and finance to logistics and retail — organizations are moving from experimental machine learning projects to production-grade AI systems. But deploying and maintaining these systems in the real world isn’t just about models and metrics — it’s about infrastructure, governance, reproducibility, automation, and reliability. That’s exactly where the principles of DevOps meet the needs of machine learning, giving birth to MLOps.

In this article, I’ll take you through my journey of transitioning from DevOps to MLOps, highlighting how our existing skills serve as a powerful foundation for this career pivot. I’ll break down key concepts, essential tools, transferable skills, new areas to explore, and real-world scenarios to help you visualize this exciting field.

What is MLOps and Why Should DevOps Engineers Care?

Machine Learning Operations (MLOps) is the discipline that brings DevOps principles to machine learning systems. While DevOps focuses on streamlining and automating software delivery processes, MLOps extends this framework to manage the entire machine learning lifecycle: from data ingestion and model training to deployment, monitoring, and retraining.

So why is MLOps critical?
Let’s look at what makes ML systems unique — and complicated:

ML models degrade over time as real-world data changes (data drift).
Model performance needs constant monitoring, far beyond basic availability checks.
ML pipelines are complex — involving data engineering, feature engineering, training, testing, deployment, and governance.
Collaboration is tougher — bridging data scientists, ML engineers, DevOps, and business stakeholders.

MLOps brings operational discipline, automation, and resilience to this rapidly evolving landscape, making it possible to reliably ship AI systems to production while keeping them accurate and accountable.

MLOps vs DevOps: The Similarities and Key Differences

Category	DevOps	MLOps
Focus	Application code and services	Data pipelines, ML models, experiments, and services
Artifacts	Application builds, container images	Datasets, models, feature sets, training scripts
Version Control	Code repositories, Docker images	Code, datasets, models, hyperparameters, pipeline configs
Pipelines	CI/CD for code integration and deployment	CI/CD/CT (Continuous Training) for models and pipelines
Monitoring	App uptime, errors, logs, performance metrics	Data quality, model performance, prediction drift
Retraining	Rare (feature updates, bug fixes)	Frequent, often automated retraining based on triggers

MLOps doesn’t replace DevOps — it builds on its principles while adding new workflows and challenges unique to the AI/ML world.

Why DevOps Engineers Are a Natural Fit for MLOps

If you’ve spent years building CI/CD pipelines, containerizing applications, or managing cloud infrastructure — you already possess critical skills that MLOps desperately needs. Let’s unpack those transferable strengths:

🔧 Infrastructure as Code (IaC)

Provisioning reliable, scalable infrastructure for ML pipelines — whether it’s a GPU training cluster or a model-serving endpoint — uses the same IaC tools you already know:

Terraform, CloudFormation, Pulumi for cloud resource provisioning
Ansible, Chef, SaltStack for environment configuration

You’ll extend this to:

GPU-enabled clusters for ML training
Distributed data processing environments (Spark, Dask, Ray)
High-performance model-serving infrastructure
Data lakes and feature stores

📦 Containerization and Orchestration

Docker and Kubernetes are the backbone of scalable MLOps. From training in isolated, reproducible environments to deploying model inference APIs, containerization ensures portability and consistency.

Training jobs run in Dockerized environments — often with specialized base images supporting GPU acceleration (NVIDIA CUDA, ROCm).
Kubeflow, MLRun, or KubeRay orchestrate ML workflows on Kubernetes.
Inference services like TensorFlow Serving, TorchServe, or custom FastAPI-based endpoints run in scalable Kubernetes deployments.

Your experience in scaling applications with Kubernetes will now extend to scaling distributed ML workloads.

🛠️ CI/CD Pipelines

MLOps adapts CI/CD into CI/CD/CT:

CI — Validate data quality, code integrity, model configurations
CD — Automate model deployments, API rollouts, and serving configurations
CT (Continuous Training) — Retrain models automatically when new data arrives or performance metrics degrade

Your experience with Jenkins, GitHub Actions, GitLab CI, or Azure DevOps will be invaluable in automating ML pipelines.

📈 Monitoring and Observability

While you’ve likely monitored application logs, latency, and error rates, in MLOps you’ll also track:

Model accuracy and F1 scores in production
Data drift and concept drift
Prediction latency and throughput
Resource utilization for training/inference workloads
Model explainability and fairness metrics

You’ll expand your observability toolkit with solutions like Prometheus, Grafana, Evidently AI, and Seldon Alibi.

New Skills to Level Up in MLOps

Even with this strong foundation, there’s a lot of new ground to cover in MLOps. Here’s what you should invest time in:

📊 Data Engineering Fundamentals

ML is powered by data. Understanding the nuances of data pipelines is crucial:

ETL/ELT workflows
Data validation tools like Great Expectations
Data versioning with DVC
Data lineage tracking with OpenLineage or Marquez

🧠 Machine Learning Fundamentals

You don’t need to become a full-time data scientist — but understanding core ML concepts will make collaboration and decision-making much easier:

Training/validation/test splits
Model evaluation metrics (accuracy, precision, recall, ROC AUC)
Overfitting, underfitting, and regularization
Hyperparameter tuning (Grid Search, Random Search, Bayesian Optimization)
Basic model types: regression, classification, clustering, etc.

📚 ML-Specific Tools and Frameworks

Familiarize yourself with the most popular tools shaping modern MLOps:

Frameworks: TensorFlow, PyTorch, Scikit-learn
Experiment Tracking: MLflow, Weights & Biases
Model Registries: MLflow Registry, SageMaker Model Registry
Pipeline Orchestration: Kubeflow Pipelines, Metaflow, Airflow
Feature Stores: Feast, Tecton
Serving Platforms: TensorFlow Serving, TorchServe, KFServing

Key MLOps Infrastructure Components

Let’s break down the building blocks of a modern MLOps system:

✅ Continuous Integration (CI) for ML

Data validation
Model code linting and tests
Automated experiment runs and evaluations

✅ Continuous Delivery (CD) for ML

Model versioning and approvals
Canary or Blue/Green model deployments
Model rollback mechanisms

✅ Continuous Training (CT)

Automated retraining pipelines
Triggered by data drift, performance decay, or new data arrival
A/B testing infrastructure for new model variants
Feedback loops from production monitoring

Continuous Training (CT): The Heart of MLOps

While CT might sound like continuous deployment — it’s far more dynamic.

What CT Does:

Detects when models need retraining (via drift detection or new data)
Automates data fetching, cleaning, feature engineering, and training
Evaluates new models against production metrics
Updates model registries and deploys better-performing models

This demands:

Pipeline orchestration tools (Kubeflow Pipelines, Airflow)
ML metadata stores (MLflow, SageMaker)
Robust validation gates (for both data and models)
A/B testing platforms for online testing

The DevOps Engineer’s Roadmap to MLOps

Quarter 1: Foundation

Learn ML basics: Andrew Ng’s Coursera or fast.ai
Set up MLflow for experiment tracking
Practice containerizing ML applications
Build simple ML pipelines with Airflow

Quarter 2: Pipelines and Deployment

Automate ML pipelines (Kubeflow, MLRun)
Integrate data validation steps
Create model registry integrations
Deploy simple inference APIs on Kubernetes

Quarter 3: Monitoring and Scaling

Implement model performance dashboards
Automate data drift detection
Build scalable inference workloads
Monitor GPU workloads and autoscaling

Quarter 4: Advanced MLOps

Integrate feature stores
Set up CT triggers based on data/metrics
Develop A/B testing infrastructure
Optimize infrastructure costs for ML

Challenges to Expect in MLOps

Data Issues: Dirty, incomplete, or biased data
Concept/Data Drift: Production data diverging from training data
Model Governance: Reproducibility, explainability, compliance
Infrastructure Complexity: GPUs, distributed training, high-availability inference
Cost Control: Balancing performance with budget constraints

Real-World MLOps in Action

Recommendation System: Daily retraining, feature store integration, A/B testing for recommendations
Computer Vision: GPU training clusters, quantized models for edge devices, drift monitoring
NLP Systems: Text preprocessing pipelines, distributed training, inference scaling, language drift detection

Conclusion

The journey from DevOps to MLOps is one of both evolution and opportunity. As AI moves from research labs to production pipelines, the skills that DevOps engineers have honed over years are becoming mission-critical in managing, scaling, and securing these systems.

By leveraging our expertise in automation, infrastructure, monitoring, and collaboration — and layering on ML-specific skills — we can build the operational backbone for tomorrow’s AI-driven applications.

Cheers,

Sim