Frameworks and software specifications in Watson Machine Learning
You can use popular tools, libraries, and frameworks to train and deploy your machine learning models and functions.
Overview of software specifications
Software specifications define the programming language and version that you use for a building a model or a function. You can use software specifications to configure the software that is used for running your models and functions. You can also define the software version to be used and include your own extensions. For example, you can use conda .yml files or custom libraries.
Supported frameworks and software specifications
You can use predefined tools, libraries, and frameworks to train and deploy your machine learning models and functions. Examples of supported frameworks include Scikit-learn, Tensorflow, and more.
For more information, see Supported deployment frameworks and software specifications.
Managing outdated frameworks and software specifications
Update software specifications and frameworks in your models when they become outdated. Sometimes, you can seamlessly update your assets. In other cases, you must retrain or redeploy your assets.
For more information, see Managing outdated software specifications or frameworks.
Parent topic: Deploying assets with Watson Machine Learning