This repository demonstrates different capabilities of IBM Watson Studio.
It provides a suite of tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data and use that data to build, train and deploy models at scale.
We will start the journey by importing a notebook for labelling anomalous data to prepare data for modelling [1]. Then, we will do a complete machine learning process starting from creating a model and ending with testing a deployed model [2]. In the third tutorial, we will build a flow to compare between multiple models [3]. Next, we will build an image classifier for pipeline inspection [4]. Finally, we will create and share an interactive dashboard which helps to discover insights from your data [5].
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Note: Select United States/US-South as a country.
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An IBM Cloud Object Storage instance
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An IBM Watson Machine Learning instance
- Step 1: Create Object Storage Instance
- Step 2: Create Machine Learning Instance
- Step 3: Create Watson Studio Instance
- Let's Get Started
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from the IBM Cloud catalog, select Object Storage.
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Click Create.
You'll be redirected to your service instance dashboard.
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from Watson category in the IBM Cloud catalog, select Machine Learning.
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Click Create.
From the left menu, click on Service credentials. Click on View credentials to save the Username and Password somewhere safe for later use.
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from Watson category in the IBM Cloud catalog, select Watson Studio.
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Click Create.
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Click on Get Started to get redirected to Watson Studio Platform.
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Create a New Project.
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Select Complete to include all available tools into the project.
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Enter a name for your project in the Name: field.
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Associate the project Storage with the object storage instance you created in step 1.
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Click on Create.
Your project's Assets page will open.
- Nailah Al-Tayyar - Developer Advocate @IBM
- Watson Studio Enablement for banking use case by Heba El-Shimy
- Predictive Industrial Visual Analysis by IBM