MLOps Pipelines with Python and AWS

Name: MLOps Pipelines with Python and AWS
Availability: InStock

Build production-grade machine learning pipelines from experiment tracking to automated deployment. Master MLflow for experiment management, AWS SageMaker Pipelines for scalable training, FastAPI and Docker for model serving, and Evidently AI for drift monitoring — the full MLOps stack used at leading tech companies.

4.70/5.0

9 hours

0 enrolled

Updated May 2026

Course Content ↓

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By J Payne

What You'll Learn

Explain the MLOps lifecycle and where each tool fits in the production pipeline

Identify experiment parameters and metrics to track with MLflow across training runs

Build an end-to-end SageMaker Pipeline for data processing, training, and model registration

Apply FastAPI and Docker to serve trained models as production REST endpoints

Automate model retraining and deployment with GitHub Actions CI/CD workflows

Evaluate model health in production using data drift and performance monitoring with Evidently AI

Prerequisites

Intermediate Python and familiarity with pandas and scikit-learn
Basic understanding of machine learning concepts: training, evaluation, and model serialization
Familiarity with Docker and the Linux command line
An AWS account with SageMaker access (free tier sufficient for exercises)

About the Instructor

J Payne

Expert instructor with hands-on industry experience in Ai Ml.

Included in paid plans

LevelIntermediate

Duration9 hours

Lessons—

Students0

Rating4.70 / 5.0

This course includes

Hands-on practice labs
AI-powered explanations
Progress tracking
Certificate of completion
Lifetime access

30-day money-back guarantee