MLOps and responsible AI practices

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MLOps and responsible AI practices

Coursera · Intermediate ·☁️ DevOps & Cloud ·3mo ago

Skills: Model Deployment90%AI Alignment Basics80%

Key Takeaways

Covers MLOps practices and responsible AI principles for deploying generative AI models

Original Description

This course equips you with the essential skills to take generative AI models from development to production. You will learn to implement robust MLOps practices on Azure, including automated CI/CD pipelines, version control, and full lifecycle management for your models. Simultaneously, you will dive into the critical principles of Responsible AI, using Microsoft’s framework to build fair, transparent, and ethical models that you can deploy with confidence.

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More on: Model Deployment

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Tutorial 11- How To Deploy End To End ML Projects In Production AWS Cloud Using CI CD Pipeline

Tutorial 11- How To Deploy End To End ML Projects In Production AWS Cloud Using CI CD Pipeline

Use Amazon SageMaker with PyTorch (Hebrew)

Use Amazon SageMaker with PyTorch (Hebrew)

Automate, Evaluate and Deploy ML Models Confidently

Automate, Evaluate and Deploy ML Models Confidently

Introducing LangSmith Studio and Deployment for LangGraph.js

Introducing LangSmith Studio and Deployment for LangGraph.js

Ryan Herr - After model.fit, before you deploy| JupyterCon 2020

Ryan Herr - After model.fit, before you deploy| JupyterCon 2020

Deploy & Optimize ML Services Confidently

Deploy & Optimize ML Services Confidently

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