Description:
Embark on a comprehensive 3-hour and 51-minute journey through an end-to-end Deep Learning project, focusing on MLOps tools like DVC and deployment using GitHub Actions on Azure and AWS. Learn to create a Chicken Disease Classification system, starting from project setup and data ingestion to model training, evaluation, and deployment. Explore crucial concepts such as logging, exception handling, and utility modules while implementing various project workflows. Gain hands-on experience with notebook experiments and their final implementations for data ingestion, base model preparation, callbacks, model training, and evaluation. Master the art of writing DVC files for pipeline tracking, creating prediction pipelines, and developing user applications. Conclude with in-depth tutorials on CI/CD deployment processes for both AWS and Azure cloud platforms.
End to End Deep Learning Project Using MLOPS DVC Pipeline With Deployments Azure and AWS - Krish Naik
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#Computer Science
#Deep Learning
#Programming
#Cloud Computing
#Amazon Web Services (AWS)
#DevOps
#CI/CD
#GitHub Actions
#Machine Learning
#MLOps
#Cloud Deployment
#Model Training
#Data Science
#Data Engineering
#Data Ingestion
#CI/CD Pipelines
#DVC
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