Learning Path: MLOps / AI Infrastructure Engineer

TL;DR

MLOps Engineers bridge the gap between data scientists who build models and the production systems where those models run. They design and maintain the infrastructure, pipelines, and processes that allow models to be trained, evaluated, versioned, deployed, monitored, and retrained reliably.

Last verified 1 April 2026

Why this matters right now

Most ML models built by data scientists never make it to production — MLOps Engineers solve this. The global MLOps market is projected to grow from $1.5 billion in 2024 to over $19.5 billion by 2032. It is one of the fastest-growing sub-fields in AI, with strong demand from every company moving AI projects from prototype to production.

How this technology has evolved

Beginner (0–4 months): Python, Git, Linux command line, Docker for containerisation, basic ML understanding (training, validation, inference), SQL. Intermediate (4–10 months): CI/CD pipelines (GitHub Actions), Kubernetes, ML experiment tracking (MLflow, Weights & Biases), model registries, cloud platforms (AWS/GCP/Azure — pick one deeply), data versioning (DVC), feature stores (Feast), workflow orchestration (Apache Airflow, Prefect). Advanced (10–18 months): End-to-end ML platform design, model monitoring (data drift detection, performance alerts with Evidently AI), Infrastructure as Code (Terraform), LLMOps (managing LLM deployments, prompt versioning, RAG monitoring), distributed training infrastructure, GPU cluster management, and cost optimisation for ML workloads.

Recommended course

Recommended starting point

This course is designed for data practitioners looking to bridge the gap between theoretical model development and functional AI application. By completing these modules, you will gain a technical grasp of how advanced machine learning architectures operate within an integrated system. It is important to note that this curriculum focuses on model proficiency rather than the operational deployment workflows or infrastructure management required for production-scale MLOps. Given the rapid shift toward moving AI projects out of the prototype phase, this material provides the essential model-building foundation required before you can effectively transition into the specialized domain of AI infrastructure engineering.

Course	Machine Learning with Artificial Intelligence
Provider	Prov alison
Level	Advanced
Cost	Free to learn, optional paid certificate

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What this means for your roadmap

Core tools: Docker, Kubernetes, GitHub Actions. Experiment tracking: MLflow, Weights & Biases. Orchestration: Apache Airflow, Prefect, Kubeflow, ZenML. Feature stores: Feast, Tecton, Hopsworks. Model serving: TorchServe, BentoML, vLLM. Monitoring: Evidently AI, WhyLabs, Grafana. Cloud ML: AWS SageMaker, Google Vertex AI, Azure ML. IaC: Terraform, Pulumi. Recommended certifications: AWS Certified DevOps Engineer, Google Professional MLOps Engineer, Microsoft DevOps Engineer Expert (AZ-400).