IBM Watson Pipelines
The Watson Pipelines editor provides a graphical interface for orchestrating an end-to-end flow of assets from creation through deployment. Assemble and configure a pipeline to create, train, deploy, and update machine learning models and Python scripts.
For information on supported regions for Watson Pipelines see Regional limitations for Cloud Pak for Data as a Service.
To design a pipeline that you drag nodes onto the canvas, specify objects and parameters, then run and monitor the pipeline.
Automating the path to production
Putting a model into a product is a multi-step process. Data must be loaded and processed, models must be trained and tuned before they are deployed and tested. Machine learning models require more observation, evaluation, and updating over time to avoid bias or drift.
Automating the pipeline makes it simpler to build, run, and evaluate a model in a cohesive way, to shorten the time from conception to production. You can assemble the pipeline, then rapidly update and test modifications. The Pipelines canvas provides tools to visualize the pipeline, customize it at run time with pipeline parameter variables, and then run it as a trial job or on a schedule.
The Pipelines editor also allows for more cohesive collaboration between a data scientist and a ModelOps engineer. A data scientist can create and train a model. A ModelOps engineer can then automate the process of training, deploying, and evaluating the model after it is published to a production environment.
Use cases and tutorials
Watson Pipelines is part of IBM's Data Fabric collection of tools and capabilities for managing and automating your data and AI lifecycle. For more information on how Data Fabric can support your machine learning goals and operations in practical ways, see Use cases. For real-world use cases and tutorials for using Watson Pipelines to orchestrate AI solutions, see:
- Data science and MLOps use case describes how to manage data, operationalize model building and deployment, and evaluate model fairness and performance.
- Data science and MLOps tutorial: Orchestrate an AI pipeline with data integration
- Data science and MLOps tutorial: Orchestrate an AI pipeline with model monitoring
Add a pipeline to your project and get to know the canvas tools.
For more information, see this blog post about automating the AI lifecycle with a pipeline flow.