Infrastructure Deep Dive
3-4 weeksSkills You'll Build
Kubernetes fundamentals for ML workloadsGPU scheduling and resource managementInfrastructure as Code (Terraform, Pulumi)Container orchestration patternsMulti-cloud deployment strategies
AI Engineer → MLOps Specialist / ML Platform Engineer
AI Engineer to MLOps Specialist: Productionizing AI at Scale
Transform from building AI models to operationalizing them at enterprise scale. As an AI Engineer, you already understand how to create intelligent systems, now learn to deploy, monitor, and scale them reliably. MLOps specialists bridge the gap between experimental notebooks and production-grade AI infrastructure. This path focuses on the systems thinking required to run AI workloads that handle millions of requests, recover gracefully from failures, and optimize costs without sacrificing performance. You will master containerization, orchestration, model serving frameworks, and observability patterns specific to ML systems. The emerging field of LLMOps receives special attention, managing foundation models presents unique challenges around context management, token costs, and latency optimization that traditional MLOps did not address. By the end, you will be able to architect ML platforms that enable entire teams to deploy models safely, implement feature stores and model registries, design CI/CD pipelines for ML artifacts, and build the monitoring dashboards that catch model drift before it impacts users. MLOps specialists command premium salaries because they solve the hardest problem in AI: making it actually work in production. Companies have learned that a perfectly trained model is worthless without the infrastructure to serve it reliably. Timeline: 4-6 months.
4-6 months
Prerequisites
- Production ML model deployment experience
- Strong Python proficiency
- LLM application development (RAG, agents, chains)
- Basic containerization (Docker fundamentals)
- Cloud platform familiarity (AWS, GCP, or Azure)
- REST API design and implementation
Your Learning Path
Advanced Model Serving
3-4 weeksSkills You'll Build
Model serving frameworks (TorchServe, Triton, vLLM)Batching strategies for inference optimizationModel compilation and quantizationA/B testing and canary deployments for modelsLoad balancing for ML endpoints
LLMOps Specialization
3-4 weeksSkills You'll Build
LLM gateway architecture (rate limiting, routing)Prompt versioning and managementContext window optimization strategiesLLM caching patterns (semantic cache, exact match)Multi-model orchestration and fallbacks
Observability & Monitoring
2-3 weeksSkills You'll Build
ML-specific metrics (latency, throughput, drift)Distributed tracing for AI pipelinesModel performance dashboardsAlerting strategies for ML systemsLog aggregation for debugging inference
Cost Optimization & FinOps
2-3 weeksSkills You'll Build
GPU cost optimization strategiesSpot/preemptible instance managementToken usage tracking and budgetingModel efficiency vs cost tradeoffsAutoscaling policies for ML workloads
ML Platform Engineering
3-4 weeksSkills You'll Build
Feature store design and implementationModel registry and artifact managementCI/CD pipelines for ML (MLflow, Kubeflow)Self-service deployment platformsMulti-tenant ML infrastructure
Portfolio & Career Positioning
2-3 weeksSkills You'll Build
Open-source MLOps project contributionsArchitecture documentation and ADRsSystem design interview preparationMLOps certification pathsBuilding reputation in MLOps community