AI System Design + MLOps + AWS Kubernetes

Ship AI systems beyond the notebook.

Build a real healthcare AI platform from raw data to a production deployment pipeline: EDA, feature engineering, MLflow experiments, DVC versioning, FastAPI serving, Docker, AWS EKS, monitoring, and drift detection.

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48K+students across courses

7xMicrosoft MVP instructor

EKSAWS Kubernetes deployment

PSIproduction drift monitoring

Production AI delivery runtime

Healthcare AI Prediction Service experiment, version, serve, monitor

Raw Dataingestion, validation, profilingpandas

EDAstatistical analysis and signal discoverynotebooks

Feature Engineeringreproducible training featurespipelines

MLflowexperiments, metrics, model registrytracking

DVCdataset and artifact versioninglineage

FastAPI + GradioAPI and user-facing inference appserving

Containerize
Docker image, reproducible runtime, release path

Observe
model quality, drift checks, PSI-based monitoring

AWS EKS production deployment

Why this course exists

Most ML projects die between notebook and production.

A model is not a system. Production AI needs reproducible data, traceable experiments, deployable services, containerized runtimes, cloud orchestration, and monitoring that catches quality decay before users do.

Data foundations

Start with raw healthcare data, exploratory analysis, feature preparation, and reliable training inputs.

Experiment discipline

Track models with MLflow, compare metrics, and move from experiments into repeatable delivery.

Version everything

Use DVC so datasets, artifacts, and model lineage are not mystery files on someone else's machine.

Serve the model

Expose predictions through FastAPI and Gradio so the AI system has a real product surface.

Deploy like software

Package the system with Docker and push it toward AWS EKS instead of stopping at localhost.

Monitor drift

Watch production signals and PSI-based drift so model quality has operational visibility.

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Production stack

Everything needed to take ML from idea to runtime.

This course connects data science, backend engineering, cloud deployment, and production monitoring into one practical AI system design workflow.

Raw Data EDA Feature Engineering Model Training MLflow DVC FastAPI Gradio Docker AWS EKS Kubernetes Monitoring Drift Detection PSI AI System Design

MLOps pipeline from data to AWS Kubernetes

"A trained model is only the beginning. The system around it decides whether it survives production."

The course is built around production workflows, not isolated notebook wins.

What you will leave with

A complete mental model for production AI.

How to structure a real ML delivery pipeline from raw data onward.

How MLflow and DVC fit into repeatable model delivery.

How to expose models with APIs and app interfaces.

How Docker and EKS change local ML scripts into deployable systems.

How monitoring and drift detection protect model quality after launch.

How to talk about AI architecture as an engineer, not just as a notebook author.

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Course path

Built as a production journey.

Each phase moves the project closer to a deployed system. You do not stop at model accuracy; you learn the architecture needed to operate the model.

Phase 1

Understand the dataEDA, cleaning, feature engineering, and model training foundations.

Phase 2

Track and versionMLflow for experiments and registry, DVC for data and artifact lineage.

Phase 3

Serve and packageFastAPI, Gradio, Docker, and reproducible application runtime.

Phase 4

Deploy and monitorAWS EKS deployment, monitoring, drift detection, and production thinking.

AI System Design & MLOps

Stop stopping at notebooks.

Build the pipeline, deploy the service, monitor the model, and understand the architecture that makes production AI real.

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