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\nDeploy Python ML Apps\n

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\n\nPublisher:\n\nAPMonitor\n\n\n
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\nTraining and testing are often performed once on a dedicated computing platform. The trained model is then packaged for another computing system for predictions. Deploying machine learning is the process of making the model available to...\nTraining and testing are often performed once on a dedicated computing platform. The trained model is then packaged for another computing system for predictions.

Deploying machine learning is the process of making the model available to produce results for people or computers to access the model predictions. It involves managing data flow to the application for training or prediction. Data flow can be in batches or a continuous stream.

The machine learning application may be deployed in many ways such as through an Application Programming Interface (API), a web-interface, an APP for mobile devices, or through a Jupyter Notebook or Python script. A few exercises demonstrate packaging and transfer of applications for deployment.

If the machine learned model is trained offline, the model is encapsulated and transferred to the hosting target. A scalable deployment means that the computing architecture can handle increased demand such as through a cloud computing host with on-demand scale-up. A Docker container can be deployed to cloud services.\n
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Resource Details

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Educator Rating
Not yet Rated
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Media Length
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23:22
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Grade
10th - Higher Ed
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Subjects
STEM
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Media Type
Instructional Videos
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Source:
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\nAPMonitor.com\n\n
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Date
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2022
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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data, time
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Additional Tags

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apmonitor, docker, container, cloud, web, python, prediction, load, pickle file, embedded devices, binary form, parameter weights, binary format, neural network, nearest neighbors, google cloud, logistic regression, image classification, data set, concrete cracks, machine learning, docker container, import pickle, tensorflow model, test data, pickle, file, image, cracks, crack, examples, package, autopilot, import
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Classroom Considerations

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Best For: Explaining a topic
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