Data Science & AIFREE COUPON
AI System Design & MLOps: From Raw Data to AWS Kubernetes
Rating
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Description
- Let’s be honest: the tech world is drowning in entry-level data scientists who can fit a model in a Jupyter Notebook but have absolutely no clue how to keep that model alive in a production environment. I’ve seen countless projects stall because the team couldn’t bridge the gap between a “good” F1-score and a scalable, cloud-native deployment. This course, “
- AI System Design & MLOps: From Raw Data to AWS Kubernetes,” is the reality check the industry needs.
- Instead of obsessing over hyperparameter tuning for the hundredth time, this course forces you to think like a Software Architect. Using an enterprise healthcare project as the backdrop is a brilliant move. Why? Because healthcare is messy. It involves sensitive data, strict governance requirements, and zero room for error. You aren’t just building a model; you’re building a robust AI system that handles data leakage, tracks versions with DVC, and lives inside an Amazon EKS (Elastic Kubernetes Service) cluster. This is the transition from “playing with data” to “engineering reliable software.” It’s an opinionated, high-velocity deep dive into the “Ops” part of MLOps that most bootcamps conveniently ignore.
What You'll Learn
- Build an end-to-end AI system from raw data to cloud deployment using real-world architectureDesign ML pipelines with SQL, feature engineering, and leakage-safe model trainingUse MLflow and DVC for experiment tracking, data versioning, and reproducible pipelinesDevelop production-ready APIs using FastAPI with validation, logging, and model loadingImplement drift detection using PSI and trigger automated retraining pipelinesContainerize applications using Docker and deploy scalable services on AWS ECR and EKSConnect data, ML, MLOps, APIs, monitoring, and cloud into one cohesive systemThink like an architect and design production-first AI systems, not just models
- The current job market is shifting. We are seeing a massive demand for MLOps Engineers and AI Architects over pure research roles. Completing a real-world project like this provides the job-ready skills that actually move the needle during technical interviews. It serves as excellent certification prep for those eyeing the AWS Machine Learning Specialty or the CKAD (Certified Kubernetes Application Developer) exams.
- Graduates of this curriculum are well-positioned for roles such as: MLOps Engineer: Bridging the gap between data science and DevOps.
- Machine Learning Engineer: Designing end-to-end ML pipelines.
- Data Architect: Managing data governance and scalable infrastructure.
- AI Technical Lead: Overseeing the transition from raw data to cloud-deployed services.
Important Notes
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