diff --git a/manifests/overlays/apps/base/kustomization.yaml b/manifests/overlays/apps/base/kustomization.yaml index 07bb957cc8..09cf79580f 100644 --- a/manifests/overlays/apps/base/kustomization.yaml +++ b/manifests/overlays/apps/base/kustomization.yaml @@ -9,5 +9,5 @@ resources: - ./anaconda-ce - ./openvino - ./pachyderm - - ./watson + - ./watson-x - ./rhoai diff --git a/manifests/overlays/apps/base/watson/kustomization.yaml b/manifests/overlays/apps/base/watson-x/kustomization.yaml similarity index 65% rename from manifests/overlays/apps/base/watson/kustomization.yaml rename to manifests/overlays/apps/base/watson-x/kustomization.yaml index 867b28870b..cebc5ea8f0 100644 --- a/manifests/overlays/apps/base/watson/kustomization.yaml +++ b/manifests/overlays/apps/base/watson-x/kustomization.yaml @@ -4,6 +4,5 @@ commonLabels: app: odh-dashboard app.kubernetes.io/part-of: odh-dashboard resources: -- deploy-watson-model-quickstart.yaml -- watson-docs.yaml -- watson-studio-app.yaml +- watson-x-docs.yaml +- watson-x-app.yaml diff --git a/manifests/overlays/apps/base/watson/watson-studio-app.yaml b/manifests/overlays/apps/base/watson-x/watson-x-app.yaml similarity index 55% rename from manifests/overlays/apps/base/watson/watson-studio-app.yaml rename to manifests/overlays/apps/base/watson-x/watson-x-app.yaml index 69cf26fb19..7fced0b914 100644 --- a/manifests/overlays/apps/base/watson/watson-studio-app.yaml +++ b/manifests/overlays/apps/base/watson-x/watson-x-app.yaml @@ -1,49 +1,45 @@ apiVersion: dashboard.opendatahub.io/v1 kind: OdhApplication metadata: - name: watson-studio + name: watson-x-ai annotations: opendatahub.io/categories: 'Model development,Model training,Data visualization,Data preprocessing,Notebook environments' spec: - displayName: IBM Watson Studio + displayName: IBM watsonx.ai provider: IBM description: >- - IBM Watson Studio is a platform for embedding AI and machine learning into your business and creating custom models with your own data. + IBM® watsonx.ai is part of the IBM watsonx AI and data platform, bringing together new generative AI capabilities powered by foundation models and traditional machine learning (ML) into a powerful studio spanning the AI lifecycle. kfdefApplications: [] route: '' - csvName: 'ibm-cp-data-operator' - serviceName: 'ibm-nginx-svc' + csvName: '' + serviceName: '' img: >- IBM Logo - docsLink: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/svc-welcome/wsl.html + docsLink: https://www.ibm.com/docs/SSQNUZ_latest/svc-welcome/watsonxai.html category: Self-managed support: third party support - quickStart: build-deploy-watson-model - getStartedLink: https://developer.ibm.com/series/cloud-pak-for-data-learning-path + quickStart: '' + getStartedLink: https://ibm.biz/wxai-get-started getStartedMarkDown: >- - # IBM Watson Studio + # IBM watsonx.ai - Build, run, and manage AI models at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. +

Train, validate, tune, and deploy generative AI solutions with foundation models in IBM® watsonx.ai.

- - Realize results faster +

In watsonx.ai, you can use large language models from IBM and other providers. The available foundation models support a range of use cases for both natural languages and programming languages.

- - Speed data science development with AutoAI +

Experiment with prompt engineering in the Prompt Lab, a tool that is designed to help you prompt foundation models. Use built-in sample prompts to get started with confidence.

- - Prepare and build models visually and programmatically +

Store effective prompts as prompt template assets that you can reuse and share with others. Or store the prompt as a notebook asset. The prompt text, model reference, and prompt engineering parameters are formatted as Python code in a notebook that you can interact with programmatically.

- - Deploy and run models through one-click integration - - - Promote AI governance with fair, explainable AI +

Use the Tuning Studio to guide a foundation model to return output that better meets your needs.

- Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. + ## Quick links - # Subscribe to the operator on Marketplace + - [Install](https://ibm.biz/wxai-install): Install the service - - [https://marketplace.redhat.com/en-us/products/ibm-watson-studio](https://marketplace.redhat.com/en-us/products/ibm-watson-studio) + - [Use](https://www.ibm.com/docs/SSQNUZ_latest/wsj/analyze-data/fm-overview.html): Work with the service - # Install the operator and validate - - - [https://marketplace.redhat.com/en-us/documentation/operators](https://marketplace.redhat.com/en-us/documentation/operators) + - [What's new](https://www.ibm.com/docs/SSQNUZ_latest/fixlist/watsonxai-fixlist.html): See a list of new features diff --git a/manifests/overlays/apps/base/watson-x/watson-x-docs.yaml b/manifests/overlays/apps/base/watson-x/watson-x-docs.yaml new file mode 100644 index 0000000000..406c8c61bf --- /dev/null +++ b/manifests/overlays/apps/base/watson-x/watson-x-docs.yaml @@ -0,0 +1,15 @@ +apiVersion: dashboard.opendatahub.io/v1 +kind: OdhDocument +metadata: + name: watson-x-use-case + annotations: + opendatahub.io/categories: 'Data preprocessing, Data visualization, Model development' +spec: + appName: watson-x-ai + type: how-to + displayName: IBM watsonx.ai use case + description: |- + Enable your enterprise to develop and deploy machine learning models and generative AI solutions. + url: https://ibm.biz/wxai-use-case + durationMinutes: 45 +--- diff --git a/manifests/overlays/apps/base/watson/deploy-watson-model-quickstart.yaml b/manifests/overlays/apps/base/watson/deploy-watson-model-quickstart.yaml deleted file mode 100644 index 415e57583e..0000000000 --- a/manifests/overlays/apps/base/watson/deploy-watson-model-quickstart.yaml +++ /dev/null @@ -1,88 +0,0 @@ -apiVersion: console.openshift.io/v1 -kind: OdhQuickStart -metadata: - name: build-deploy-watson-model - annotations: - opendatahub.io/categories: 'Model development,Model serving,Getting started,Model training' -spec: - displayName: Deploying a model with Watson Studio - appName: watson-studio - durationMinutes: 15 - icon: >- - - IBM Logo - - - description: Import a notebook in Watson Studio and use AutoAI to build and deploy a model. - introduction: |- - ### This quick start walks you through importing a Notebook in Watson Studio, building a model with AutoAI, and deploying a model. - Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, - the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. - - tasks: - - title: Create a project in Cloud Pak for Data - description: |- - ### Create a project - 1. Click **Projects** → **View all projects** from the menu. - 2. Click **New project**. - 3. Select **Analytics project** and click **OK**. - 4. Add a title and description, then click **Create**. - summary: - success: You have created a new project - failed: Try the steps again - - title: Add data to your project - description: |- - ### After you create a project, add data assets to it so that you can work with data - 1. From your project’s **Assets** page, click **Add to project** → **Data**. - 2. In the **Load** pane that opens, browse for a CSV file or drag it from your file manager onto the pane. - summary: - success: The files are listed as data assets on the **Assets** page of your project. - failed: Try the steps again - - title: Import a notebook into your project - description: |- - ### After you have data assets you are ready to import a notebook - 1. From your project, click **Add to Project** → **Notebook**. - 2. On the **New Notebook** page, upload a notebook file from your file system, or by using a URL. - 3. Specify the runtime environment for the language you want to use (Python, R, or Scala). - 4. Click **Create Notebook**. - summary: - success: The notebook opens in the Jupyter notebook editor. - failed: Try the steps again - - title: Load data into your notebook - description: |- - ### After you have a notebook created you can load data into the notebook - 1. Click in an empty code cell in your notebook. - 2. Click the **Find and Add Data** icon. - 3. Click **Insert to code** → **pandas DataFrame** right below the data file name. - 4. Run the cell. - summary: - success: The data is now loaded into the notebook and you can see a preview of the data. Run the rest of the notebook to train your model. - failed: Try the steps again - - title: Train an AutoAI model - description: |- - ### As an alternative to the notebook, you can build a model with AutoAI - 1. From the **Assets** page of your project, click **Add to Project** → **AutoAI experiment**. - 2. Name your experiment, then click **Create**. - 3. Upload or add a CSV file from project to train the experiment. - 4. Select the prediction column. - 5. Run the experiment. - summary: - success: You have trained a model with AutoAI. - failed: Try the steps again - - title: Save and deploy a model - description: |- - ### After a model is trained with AutoAI, you can deploy it - 1. After the AutoAI experiment finishes training, select the best performing pipeline and click **Save as model**. - 2. A notification indicates the model is saved. Click the **View in project** link in the notification to open the model details page. - 3. Create a deployment space, and then promote the model to the deployment space. - 4. Click the link in the success notification to open the model in the deployment space. - 5. Create and name a new deployment of the model. - 6. When the deployment is ready, click the deployment name and choose **Online** as the deployment type, assigning a name for the deployment. - 7. When the deployment is ready, click the name to view and test the deployment. - 8. Click the **Test** tab and use the form interface to enter test values. - 9. Click **Predict** to view the prediction. - summary: - success: You have deployed an AutoAI model. - failed: Try the steps again - conclusion: You are now able to import a notebook in Watson Studio, build, and deploy a model. - nextQuickStart: [] diff --git a/manifests/overlays/apps/base/watson/watson-docs.yaml b/manifests/overlays/apps/base/watson/watson-docs.yaml deleted file mode 100644 index 94f0919981..0000000000 --- a/manifests/overlays/apps/base/watson/watson-docs.yaml +++ /dev/null @@ -1,195 +0,0 @@ -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: auto-ai-experiment-model-tutorial - annotations: - opendatahub.io/categories: 'Model development,Model training' -spec: - appName: watson-studio - type: tutorial - displayName: Running an AutoAI experiment to build a model - description: |- - Watch a video about building a binary classification model for a marketing campaign. - url: https://video.ibm.com/channel/23952663/video/cpd35-ws-autoai - durationMinutes: 8 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: build-binary-classification-model-tutorial - annotations: - opendatahub.io/categories: 'Model development,Model training' -spec: - appName: watson-studio - type: tutorial - displayName: Building a binary classification model - description: |- - Train a model to predict if a customer is likely to subscribe to a bank promotion. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/analyze-data/autoai_example_binary_classifier.html - durationMinutes: 10 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: jupyter-notebooks-in-watson-studio-tutorial - annotations: - opendatahub.io/categories: 'Notebook environments,Model development' -spec: - appName: watson-studio - type: tutorial - displayName: Using Jupyter notebooks in Watson Studio - description: |- - Watch a video about working with Jupyter notebooks in Watson Studio. - url: https://video.ibm.com/channel/23952663/video/cpd35-ws-notebook-basics - durationMinutes: 3 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: python-notebook-generate-results-open-scale-tutorial - annotations: - opendatahub.io/categories: 'Model training,Model serving,Model development' -spec: - appName: watson-studio - type: tutorial - displayName: Run a Python notebook to generate results in Watson OpenScale - description: |- - Run a Python notebook to create, train, and deploy a machine learning model. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/model/wos-tutorial-adv.html - durationMinutes: 15 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: watson-studio-clean-shape-visualize-how-to - annotations: - opendatahub.io/categories: 'Data management,Data preprocessing,Data cleaning,Data visualization' -spec: - appName: watson-studio - type: how-to - displayName: How to clean, shape, and visualize data - description: |- - Learn how to clean and shape tabular data using Watson Studio data refinery. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/refinery/refining_data.html - durationMinutes: 10 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: watson-studio-create-connection-how-to - annotations: - opendatahub.io/categories: 'Data management' -spec: - appName: watson-studio - type: how-to - displayName: How to create a connection to access data - description: |- - Learn how to create connections to various data sources across the platform. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/cpd/access/connect-data-sources.html - durationMinutes: 10 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: watson-studio-create-deployment-space-how-to - annotations: - opendatahub.io/categories: 'Model serving' -spec: - appName: watson-studio - type: how-to - displayName: How to create a deployment space - description: |- - Learn how to create a deployment space for machine learning. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/analyze-data/ml-spaces_local.html - durationMinutes: 10 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: watson-studio-create-notebook-how-to - annotations: - opendatahub.io/categories: 'Notebook environments,Getting started' -spec: - appName: watson-studio - type: how-to - displayName: How to create a notebook in Watson Studio - description: |- - Learn how to create a basic Jupyter notebook in Watson Studio. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/analyze-data/creating-notebooks.html - durationMinutes: 3 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: watson-studio-create-project-how-to - annotations: - opendatahub.io/categories: 'Getting started,Notebook environments' -spec: - appName: watson-studio - type: how-to - displayName: How to create a project in Watson Studio - description: |- - Learn how to create an analytics project in Watson Studio. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/getting-started/projects.html - durationMinutes: 5 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: watson-studio-integrate-with-git-how-to - annotations: - opendatahub.io/categories: 'Data management,Getting started,Notebook environments' -spec: - appName: watson-studio - type: how-to - displayName: How to create a project that integrates with Git - description: |- - Learn how to add assets from a Git repository into a project. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/manage-data/git-integration.html - durationMinutes: 10 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: watson-studio-load-data-how-to - annotations: - opendatahub.io/categories: 'Data management,Getting started,Notebook environments' -spec: - appName: watson-studio - type: how-to - displayName: How to load data into a Jupyter notebook - description: |- - Learn how to integrate data sources into a Jupyter notebook by loading data. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/analyze-data/load-and-access-data.html - durationMinutes: 5 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: watson-studio-runtime-env-how-to - annotations: - opendatahub.io/categories: 'Notebook environments,Getting started' -spec: - appName: watson-studio - type: how-to - displayName: How to choose between notebook runtime environment options - description: |- - Explore available options for configuring your notebook runtime environment. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/analyze-data/notebook-environments.html - durationMinutes: 5 ---- -apiVersion: dashboard.opendatahub.io/v1 -kind: OdhDocument -metadata: - name: watson-studio-setup-openscale-how-to - annotations: - opendatahub.io/categories: 'Model monitoring,Getting started' -spec: - appName: watson-studio - type: how-to - displayName: How to set up Watson OpenScale - description: |- - Learn how to track and measure outcomes from models with OpenScale. - url: https://www.ibm.com/support/knowledgecenter/SSQNUZ_3.5.0/wsj/model/getting-started.html - durationMinutes: 10 ----