A Python programmer's guide to getting started with IBM Watson Studio. It covers setting up environments, running Jupyter notebooks, and deploying models.
IBM Watson Studio is a cloud-based data science platform that provides a managed environment for Jupyter notebooks, AutoAI pipelines, and model deployment. For Python programmers already comfortable with scikit-learn and pandas, the learning curve is moderate. The tools are familiar, but the deployment story is different.
Setting up a project begins with creating a Cloud Object Storage instance and linking it to a Watson Studio project. From there, you can launch a Jupyter environment with your choice of Python runtime, GPU support, and pre-installed libraries. The environment behaves like a standard JupyterLab instance, with the addition of IBM's ibm_watson_machine_learning SDK for pushing models to production.
Deploying a model involves serialising it (typically with joblib or pickle), uploading it to the project's storage, and creating a deployment space. Watson Studio then exposes a REST endpoint you can call from any application, with built-in logging and monitoring through IBM OpenScale.
