ZerotoMastery – MLOps Bootcamp: Build Real-World AI Infrastructure
Released 9/2026
Taught by Patrik Szepesi
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 68 Lessons ( 8h 58m ) | Size: 1.9 GB
MLOps is the discipline that combines AI/Machine Learning, Data Engineering, and DevOps to reliably deploy, monitor, and maintain AI models. You’ll learn to go beyond model training and learn how real AI systems operate in production by connecting AWS services into a modern MLOps stack that take an AI model from experimentation to a live, continuously improving, production-ready AI application that scales.
What you’ll learn
Understand the full MLOps lifecycle from training to retraining
Fine-tune Vision Transformers with Hugging Face on AWS
Prepare S3 datasets for cloud-based model training
Deploy real-time AWS endpoints for live predictions
Monitor model confidence and activity with CloudWatch
Build AWS Pipelines for automated ML workflows
Connect Lambda, API Gateway, S3 and Bedrock
Retrain models automatically and gate production deployments
You probably already know that one of the most in-demand skills in the technology workplace right now is being able to train and train and fine-tune an AI/ML model.
But having the skills to deal with what happens after that is what will actually make you indespensible and advance your career.
Those skills are building and managing the infrastructure and systems that the AI needs to be deployed, to continually improve, and to scale with a company’s needs.
In this MLOps Bootcamp you’ll learn exactly those skills!
You’ll build the systems that take an AI/ML model from experimentation to a live, continuously improving, production-ready AI application that scales.
You’ll fine-tune a Vision Transformer with Hugging Face and SageMaker, deploy it behind a real-time endpoint, capture prediction data and monitor model behavior with CloudWatch.
You’ll build real-world skills with Lambda, API Gateway, S3 and Bedrock to collect low-confidence predictions, label new examples and feed them back into an automated retraining workflow.
You’ll also build evaluation and deployment gates so a newly trained model doesn’t automatically replace the production model just because it’s newer. It has to perform better first.
By the end, you’ll understand how the pieces of a modern MLOps stack connect and be ready to build the AI infrastructure needed for now and the future.
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