Practice creating ML services on cloud

Starting your first machine learning (ML) project on the cloud might seem intimidating, especially if you're new to the platform or the technology. Here is the list of resources to overcome your fear and start building your first ML projects on cloud:

Deploy ML models at 3 big cloud platforms

Very simple tutorial, which shows you how to deploy serialized model and monitoring on AWS, GCP and Azure

GCP

Deploy ML model on GCP (Tutorial from Nvidia) Wrap model in a Flask application and deploy on Google Cloud End-to-end system on GCP (Advanced) Covers the entire data science lifecycle (DSLC), from data collection and training a model to enabling batch inference and monitoring model performance over time.

AWS

end-to-end MLOps pipeline (official tutorial from Amazon)

Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions

Azure

MLOps with Azure (official tutorail from Microsoft) Learning path, where in 4 hours you can learn how to implement key concepts like source control, automation, and CI/CD to build an end-to-end MLOps solution.

Introduction to Azure ML

Beginner-friendly introduction to Azure environment for building and managing machine learning models.

Happy building!