2. End-to-End MLOps Project using one component on Azure | End to End Deployment | Data Science
This Playlist contains an Understanding of MLpos and related projects on cloud!
DevOps brings together the best practices for software development and engineering, quality assurance, and IT operations.
MLOps will ensure model governance is established as a key part of the process, and that the model risks are clearly understood. This fits the machine learning model into wider conversations on risk management within the organization. It also frames the model as a tool to achieve wider business objectives. There is a range of MLOps software options available to help track the MLOps process.
MLops tutorial - End to End with the project on different concepts.
Notebook used: https://github.com/codehax41/Mlops
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Terms:
Workspace: A workspace is a block or you can say resource group where we will be performing our ml operation or experiments
Experiment: Are basically a place where we will see our individual model run resources
Run: A run represents a single trial in experiments. For example, you running the first run and there you have 3 combinations if alpha value. And then for that experiments, you can compare generated artifacts
Model: is a place like a registry where you will see all the models registered and their name with version
Compute Target: is a class for creating and managing VMs or Azure ML
Pipeline: Is an automated workflow where we automate operations step by step Also can be scheduled (for example prep, trans, train, test validate, deploy)
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#MachineLearning, #MLops #mlops, #Mlflow, #Azure, #AzureML, #YouTubeLearning,
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All Playlist in my channel
Machine Learning Playlist: • Machine Learning Beginner to Expert || End...
Deep Learning Playlist: • Playlist
AI Projects Playlist: • Playlist
Stats & Probability Playlist: • Statistics & Probability for Data Scince
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Connect with me here:
Github: https://github.com/codehax41
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THANKS & Love you all!!! ❤️
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