MLOps Crash Course for Beginners - Part 2 | Deploy, Monitor, and Scale Machine Learning Models

Welcome to Part 2 of the M-L-Ops Crash Course — your hands-on guide to taking trained models into full production and keeping them running reliably at scale.

In this video, you’ll learn how modern MLOps teams deploy, monitor, and optimize machine learning systems in real business environments.

🚀 What You’ll Learn:

Model deployment strategies: batch, online, and streaming

Serving models with MLflow Serve, TensorFlow Serving, and TorchServe

Cloud deployment using AWS, Google Cloud, and Azure

Container orchestration with Docker Compose and Kubernetes

Model monitoring and drift detection

Building automated pipelines with Airflow, Prefect, and Kubeflow

Automated retraining and redeployment

Scaling inference workloads and caching for performance

Managing model versions and features with MLflow and Feast

By the end of this video, you’ll know how to deploy machine learning models confidently, monitor their performance in real time, and scale them efficiently across production systems.

💡 Who It’s For:
Beginners, data scientists, and developers who want to master real-world MLOps practices and become job-ready for production-level machine learning engineering roles.

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