Introduction to AWS SageMaker Training Jobs
Running ML training in the cloud with SageMaker.
MLOps106
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Cloud Storage for ML: S3 and GCS Basics
Your models and datasets need a home that isn't your laptop. Today we set up S3 buckets, lock down access with IAM, move files with boto3, and connect DVC straight to the cloud.
Introduction to Cloud Platforms for MLOps
Overview of AWS, GCP, and Azure ML services.
Serving Models with MLflow Models Serve
Move from experimentation to production by spinning up a REST API for your trained model with a single MLflow command. Learn mlflow models serve, model signatures, input schemas, and how to send real-time predictions.
Model Serving Fundamentals & REST APIs
A trained model sitting in a .pkl file helps nobody. Today your iris classifier gets a front door: a REST API that any application, in any language, can call over HTTP.
Review Week 2 & The Containerized ML Experiment Workflow
Two weeks in, you have all the pieces: Git and DVC for versioning, MLflow for tracking, and Docker for reproducible environments. A single docker compose up that spins up a tracking server, runs a versioned experiment, and logs every result.