Model Deployment
Serve ML models as REST APIs — containerised with Docker, deployed to the cloud.
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After this skill you can…
- Wrap a model in a FastAPI endpoint
- Containerise and deploy to a cloud service
- Implement batch and online inference pipelines
Prerequisites
Watch (10 videos)
Tutorial 11- How To Deploy End To End ML Projects In Production AWS Cloud Using CI CD Pipeline
→ Deploy ML models on AWS Cloud→ Use CI/CD pipelines for automated deployment
Use Amazon SageMaker with PyTorch (Hebrew)
→ Deploy a PyTorch model with SageMaker→ Train a model remotely using SageMaker
Run AI Models Inference on Amazon SageMaker HyperPod EKS | Amazon Web Services
→ Deploy AI models on Amazon SageMaker HyperPod EKS→ Use HyperPod Inference Operator for model inference
Day 3- MLOPS End To End Implementation With Deployment- Machine Learning
→ Deploy ML models→ Implement MLOPS pipelines
How to deploy machine learning model as an app in Python using Gradio
→ Deploy a machine learning model as a web app→ Use Gradio for model deployment
Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
→ Deploy scikit-learn models→ Create APIs for machine learning models
Deploy Any Machine Learning (or Deep Learning) Endpoint on Google Cloud Platform In 10 minutes
→ Deploy models on cloud platforms→ Use Cloud Run for deployment
Deploy transformer models on IoT chips
→ Deploy models on IoT chips→ Manage model deployment pipelines
Deploying ML Models & LLMs on GCP with MLOps: A Practical Demo
→ Deploy ML models to GCP→ Use MLOps for model deployment
MLOps Full Course for [2026] -12 hour | MLOps for Beginners | What is MLOps? | Edureka Live
→ Deploy machine learning models to production→ Manage model versions→ Monitor model performance
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