How to Become an MLOps Engineer

An MLOps Engineer builds and maintains the infrastructure that allows machine learning models to run reliably in production. They create the pipelines, automation, and monitoring systems that transform ML experiments into production services.

What makes this role unique: While ML Engineers focus on building and training models, MLOps Engineers focus on everything around the model - data pipelines, training infrastructure, model serving, monitoring, and CI/CD. You enable data scientists and ML engineers to deploy models without worrying about infrastructure.

Best suited for: DevOps engineers who want to specialize in ML, or software engineers who enjoy infrastructure more than model development. Best for those who get satisfaction from building reliable, scalable systems rather than optimizing model accuracy.

Explore Machine Learning degree programs to understand ML fundamentals, or Computer Science programs for strong software engineering foundations.

Your day revolves around keeping ML systems running reliably while improving the infrastructure that powers them. Expect a mix of incident response, pipeline development, and cross-team collaboration.

Morning: Check overnight model performance alerts and production metrics dashboards. A recommendation model's latency spiked - investigate whether it's a model issue or infrastructure problem. Daily standup with the ML platform team.

Afternoon: Continue building a new feature store integration for the data science team. Review a pull request for a training pipeline change. Meet with ML engineers to scope requirements for a new model serving architecture.

Core responsibilities include:

• Building and maintaining ML training pipelines
• Managing model deployment and serving infrastructure
• Creating CI/CD pipelines for ML models
• Implementing model monitoring and alerting systems
• Managing feature stores and data pipelines
• Optimizing ML infrastructure for cost and performance
• Supporting data scientists with experimentation infrastructure
• Handling production incidents involving ML systems

Common meetings: Platform team standups, incident reviews, architecture discussions with ML engineers, and cross-functional planning with data teams.

Infrastructure & Orchestration:

• Kubernetes: Container orchestration for ML workloads.
• Docker: Containerization for reproducible ML environments.
• Terraform/Pulumi: Infrastructure as code for ML platforms.
• Airflow/Prefect/Dagster: Workflow orchestration for data and training pipelines.

ML Platform Tools:

• MLflow: Experiment tracking, model registry, and deployment.
• Kubeflow: End-to-end ML pipelines on Kubernetes.
• Weights & Biases: Experiment tracking and collaboration.
• DVC: Data version control for ML projects.

Model Serving:

• Triton Inference Server: High-performance GPU inference.
• TensorFlow Serving: Serving TensorFlow models at scale.
• Seldon Core: ML deployment on Kubernetes.
• BentoML: Framework-agnostic model serving.

Feature Stores & Data:

• Feast: Open source feature store.
• Tecton: Enterprise feature store platform.
• dbt: Data transformation for ML pipelines.

Monitoring & Observability:

• Prometheus/Grafana: Infrastructure and model metrics.
• Evidently AI: ML model monitoring and drift detection.
• Arize: ML observability platform.

Cloud and Kubernetes certifications are the most valuable for MLOps roles, demonstrating infrastructure expertise.

Cloud ML certifications:

• AWS Machine Learning Specialty ($300): Validates SageMaker and AWS ML services.
• Google Professional Machine Learning Engineer ($200): GCP Vertex AI and ML pipeline expertise.
• Azure AI Engineer Associate ($165): Microsoft Azure ML certification.

Infrastructure certifications:

• Certified Kubernetes Administrator (CKA) ($395): Essential for Kubernetes-based ML platforms.
• AWS Solutions Architect Professional ($300): Deep AWS infrastructure knowledge.
• HashiCorp Terraform Associate ($70): Infrastructure as code certification.

Your portfolio should demonstrate end-to-end ML infrastructure skills, not model building.

Projects that demonstrate MLOps skills:

• End-to-end ML pipeline with training, validation, and deployment automation
• Feature store implementation with online and offline serving
• Model monitoring dashboard with drift detection and alerting
• Kubernetes-based model serving platform with autoscaling
• CI/CD pipeline for ML that includes model testing and validation
• Cost optimization project showing infrastructure efficiency gains

Key metrics to highlight:

• Deployment frequency: How often can you deploy models?
• Lead time: Time from code commit to production
• Recovery time: How fast can you roll back a bad model?
• Infrastructure cost: Cost per prediction or per training run

MLOps interviews focus on infrastructure design, DevOps practices, and ML-specific operational concerns.

System design questions:

• Design a model training pipeline that handles 100TB of data
• Design a feature store with real-time and batch serving
• Design a model serving system handling 1M predictions/second
• How would you implement A/B testing for ML models?
• Design a model monitoring system that detects drift and triggers retraining

Technical questions:

• Explain the difference between online and offline feature serving
• How do you handle model versioning and rollback?
• What's the difference between data drift and concept drift?
• How would you debug a model that's performing worse in production than in training?
• Explain canary deployments for ML models

Coding questions: Expect Python coding for automation, data processing, and API development. May include Kubernetes YAML or Terraform configuration reviews.

Common challenges:

• On-call burden: ML systems break in production. Expect to be paged for model performance issues, not just infrastructure failures.
• Tool fragmentation: The MLOps ecosystem has dozens of overlapping tools. Evaluating and choosing the right ones is exhausting.
• Data scientist friction: Data scientists may resist productionization requirements. You'll need patience to enforce best practices.
• Undefined scope: MLOps is still a new discipline. Role boundaries with ML engineering, data engineering, and DevOps can be unclear.
• Technical debt: ML systems accumulate debt quickly - outdated models, orphaned pipelines, and undocumented infrastructure.

How experienced MLOps engineers handle these:

• Build robust monitoring to catch issues before they page you
• Standardize on a core set of tools rather than chasing every new platform
• Create self-service tools that make best practices easy for data scientists
• Define clear ownership boundaries with adjacent teams
• Implement infrastructure-as-code and documentation requirements