Watson Machine Learning key terms
Here are key terms related to IBM Watson Machine Learning and IBM Watson Studio.
Term | Description and related terms |
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IBM Cloud |
The IBM cloud computing platform. Online infrastructure that enables you to use software as a service, without installing it on your computer. See: IBM Cloud platform overview Related terms:
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IBM Cloud service |
In the IBM Watson Machine Learning and Watson Studio documentation, the term service refers a service in IBM Cloud, such as IBM Watson Machine Learning or Cloud Object Storage. Related terms:
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Command line interface (CLI) |
A mechanism for working with IBM Cloud services from a command prompt on your local computer. Related terms:
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Repository |
Associated with your Watson Machine Learning service is storage for saving artifacts, such as: training definitions and models. Note that the repository is not the same things as the cloud object storage where you store your training data or training output results. Related terms:
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Training run |
Training a model can be so computationally intensive that training a model on your local computer or in a notebook might take too long or fail. So, the Watson Machine Learning service provides a mechanism to upload your model-building code and then run the training on the Watson servers. In the Watson Machine Learning and Watson Studio documentation, the term training run is used in two, related ways:
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Experiment |
In the Watson Machine Learning and Watson Studio documentation, the term experiment is used in two, related ways:
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Model |
In the Watson Machine Learning and Watson Studio documentation, the term model is used to refer to both models that apply machine learning algorithms as well as to neural networks. With Watson Machine Learning and Watson Studio, you work with models in several ways: design, build, train, store, deploy. Related terms:
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Deployment |
A deployment is an artifact in your Watson Machine Learning service through which tools and apps can access trained models. Related terms:
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Custom components |
You can define your own transformers, estimators, functions, classes, and tensor operations for use in models you deploy in Watson Machine Learning. In the Watson Machine Learning and Watson Studio documentation, the term custom components refers to transformers, estimators, and so on, that you create. Related terms:
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