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MLOps / AI Engineer Roadmap
Complete MLOps roadmap from Python and ML basics to Kubernetes-based model training, serving, LLM infrastructure, and production monitoring. The fastest-growing DevOps specialization in 2026.
6–10 months
8 phases
FoundationIntermediateAdvancedExpert
Phase 1
Python & ML Fundamentals
The language and math behind every ML system
Foundation4–6 weeks
What to learn
- Python — data types, functions, OOP, async patterns
- NumPy and Pandas — data manipulation and analysis
- Scikit-learn — training, evaluation, pipelines, cross-validation
- ML fundamentals — supervised, unsupervised, metrics (AUC, F1, RMSE)
- Deep learning basics — PyTorch or TensorFlow
- Data versioning with DVC — track datasets like code
Key tools
PythonPyTorchscikit-learnJupyterDVC
Phase 2
Containers for ML
Package reproducible ML environments
Foundation2–3 weeks
What to learn
- Docker for ML — CUDA base images, GPU passthrough
- Multi-stage builds — small training image vs serving image
- Managing Python dependencies — Poetry, pip-tools, Conda
- Container registries — ECR, GHCR for model images
- GPU-aware Dockerfiles — nvidia/cuda base images
- Reproducibility — pin OS, CUDA version, library versions
Key tools
dockernvidia-dockerECRGHCRPoetry
Phase 3
Kubernetes for ML Workloads
Run training jobs and serve models at scale
Intermediate3–4 weeks
What to learn
- GPU node groups — NVIDIA device plugin setup on EKS/GKE
- Kubernetes Jobs and CronJobs for training workloads
- Resource requests for GPU — nvidia.com/gpu limit
- Node selectors and tolerations for GPU-only pods
- Persistent storage for datasets and model checkpoints
- Karpenter for scale-to-zero GPU nodes — pay only when training
Key tools
kubectlNVIDIA device pluginEKSKarpenterHelm
Phase 4
ML Pipeline Orchestration
Automate training, evaluation, and promotion
Intermediate3–4 weeks
What to learn
- Kubeflow Pipelines — component authoring, DAGs, caching
- Apache Airflow — DAGs, operators, sensors for ML workflows
- Argo Workflows — Kubernetes-native lightweight pipeline engine
- Pipeline steps — data validation, training, evaluation, registration
- Parameterizing pipelines — hyperparameters, dataset versions
- Triggering — schedule, data arrival, model drift, PR
Key tools
KubeflowAirflowArgo WorkflowsPrefectZenML
Phase 5
Experiment Tracking & Model Registry
Know what you trained and reproduce it exactly
Intermediate2–3 weeks
What to learn
- MLflow — tracking experiments, logging metrics, artifacts
- MLflow Model Registry — versioning, staging → production promotion
- Weights & Biases — advanced experiment tracking and visualization
- Comparing runs — finding best model by metric automatically
- Model metadata — dataset version, code commit, hyperparams
- A/B model comparison — statistically valid selection
Key tools
MLflowWeights & BiasesNeptuneDVC
Phase 6
Model Serving & Inference
Get your model serving real traffic reliably
Advanced3–4 weeks
What to learn
- REST vs gRPC serving — latency and throughput tradeoffs
- KServe — Kubernetes-native model serving with canary support
- Triton Inference Server — multi-framework GPU-optimized serving
- Dynamic batching — improve throughput for offline inference
- Model quantization — INT8, FP16 for 4x faster inference
- Canary deployments for models — gradual traffic shifting
Key tools
KServeTritonBentoMLRay ServeFastAPI
Phase 7
LLM Ops & AI Infrastructure
The fastest-growing MLOps specialty in 2026
Advanced3–4 weeks
What to learn
- Running LLMs on Kubernetes — Ollama, vLLM, LocalAI
- LLM serving — OpenAI-compatible APIs, batch inference
- RAG pipelines — vector DBs (Qdrant, Weaviate) + LLMs
- LLM observability — token usage, latency, cost per query (LangFuse)
- Fine-tuning infrastructure — LoRA, QLoRA on GPU clusters
- Prompt engineering and automated evaluation at scale
Key tools
OllamavLLMQdrantLangChainLangFuse
Phase 8
ML Monitoring & Model Health
Your model degrades silently — catch it before users do
ExpertOngoing
What to learn
- Data drift — input distribution changes over time
- Model drift — prediction quality degradation detection
- Infrastructure monitoring — GPU utilization, latency, throughput
- Alerting on model quality metrics alongside system metrics
- Automated retraining triggers — drift threshold → pipeline run
- Shadow mode — run new model in parallel before full cutover
Key tools
PrometheusGrafanaEvidently AIArizeWhyLabs
Interview Prep
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Every topic on this roadmap has interview questions in the bundle — Docker, Kubernetes, AWS, CI/CD, Linux, SRE, FinOps, System Design. Grab it before your next interview.
Frequently Asked Questions
Common questions about the MLOps / AI Engineer roadmap
1What is MLOps and why does it matter?
MLOps (Machine Learning Operations) applies DevOps principles to machine learning — CI/CD for models, automated training pipelines, model versioning, serving, and monitoring. Without MLOps, ML models sit in Jupyter notebooks and never reach production reliably.
2How long does it take to learn MLOps?
With Python and basic ML knowledge, plan 4–6 months for core MLOps: containers, Kubernetes for ML, pipeline orchestration (Kubeflow/Airflow), experiment tracking (MLflow), and model serving. Add 2–3 months for LLMOps and AI infrastructure.
3Do I need to know machine learning to do MLOps?
You need enough ML knowledge to understand what you're deploying — model types, training vs inference, data pipelines, and evaluation metrics. You don't need to be a researcher. MLOps engineers are infrastructure specialists who work closely with data scientists.
4What is the MLOps engineer salary in 2026?
MLOps is the highest-paying DevOps specialization. In the US: junior MLOps $120K–$160K, mid-level $170K–$230K, senior at top AI companies $280K–$450K+ TC. In India: ₹18L–₹80L+ at top AI/tech companies.
5What tools should an MLOps engineer know in 2026?
Core tools: Docker, Kubernetes (with GPU support), MLflow or W&B for experiment tracking, Kubeflow or Airflow for pipelines, KServe or Triton for serving, and Prometheus/Grafana for monitoring. For LLMOps: Ollama, vLLM, LangFuse, and Qdrant.