Guide to Production MLOps on AWS Surfaces

The guide's emphasis on Terraform highlights a critical industry shift towards Infrastructure as Code (IaC) in MLOps. Companies adopt IaC to create reproducible, version-controlled environments, ensuring that the infrastructure for training, staging, and production is consistent, which is a core tenet of reliable MLOps on cloud platforms like AWS. Amazon EKS (Elastic Kubernetes Service) is specified for its ability to scale complex AI/ML workloads with fine-grained control. It allows engineering teams to manage everything from GPU-backed instances for training to cost-effective inference servers, using a unified orchestration platform that builds on existing Kubernetes expertise. The combination of Prometheus for metrics collection and Grafana for visualization has become a standard for monitoring ML models in production. This stack enables engineers to create dashboards that track not just system metrics like CPU usage, but also crucial model performance indicators like prediction latency and concept drift, with alerts to flag issues in real-time. Vector databases like Qdrant are central to modern AI, particularly for applications involving semantic search and Retrieval-Augmented Generation (RAG). Qdrant is designed to efficiently index and query high-dimensional vector data, making it a key component for building advanced recommendation systems, anomaly detection, and AI agents. For ML system design interviews at top tech companies, the focus is on the end-to-end lifecycle. Candidates are expected to discuss trade-offs between model accuracy, latency, and cost, and to architect the full pipeline from data ingestion and feature engineering to model deployment, scaling, and monitoring. While deep expertise in niche algorithms is less common, Data Structures and Algorithms (DSA) interviews for ML engineers typically test medium-level problems. The goal is to assess clear problem-solving, clean coding practices, and a solid understanding of time and space complexity (Big O), which is vital for building scalable ML systems. To build a standout portfolio, new graduates should create projects that mirror production environments. Ideas include deploying a fraud detection model that handles imbalanced data or a real-time recommendation engine, served via a FastAPI endpoint and containerized with Docker. A complete end-to-end MLOps project is a significant differentiator for aspiring ML engineers. This involves automating the entire lifecycle—from code commit to model deployment—using CI/CD tools like GitHub Actions to automatically trigger testing, model retraining, and deployment, demonstrating a comprehensive understanding of production workflows.