Build and scale trusted AI on any cloud. Automate the AI lifecycle for ModelOps.
IBM Watson Studio empowers data scientists, developers and analysts to build, run and manage AI models, and optimize decisions anywhere on IBM Cloud Pak for Data. Unite teams, automate AI lifecycles and speed time to value on an open multicloud architecture.
Bring together open source frameworks like PyTorch, TensorFlow and scikit-learn with IBM and its ecosystem tools for code-based and visual data science. Work with Jupyter notebooks, JupyterLab and CLIs — or in languages such as Python, R and Scala.
Overview
In this W7067G: Watson Studio Methodology course, you will explore data preparation, data modeling, data visualization, and data cataloging using Watson Studio, Watson Knowledge Catalog, and Watson Machine Learning.
Skills Covered
- Data science and AI
- Watson Studio
- Watson Machine Learning
- Watson Knowledge Catalog
- Data refinement
- Data modeling
- Data science with notebooks
- Model deployment
Prerequisites
There are no pre-requisites required to attend this course.
Target Audience
Data scientists, data engineer, business analyst
Course Outline
- Data science and AI
- Describe the value of artificial intelligence
- Explain the AI ladder approach and AI lifecycle
- Identify the roles for working with data and AI Watson Studio
- Summarize the benefits of Watson Studio
- Outline the integration of Watson Studio and Watson Machine Learning
- List and explain the tools available in Watson Studio
- Sign up for a free IBM Watson account Watson Machine Learning
- Describe machine learning methods and how they fit with AI
- Create a Watson Studio project for learning models Watson Knowledge Catalog
- Explain the features of Watson Knowledge Catalog
- Identify the role of data policies to govern data assets
- List and describe the data files used in this course
- Create a catalog, add assets to a catalog, and add catalog assets to a project Data refinement
- List the steps to successful data mining
- Describe the typical customer churn business problem
- Identify the steps in the data refinement process
- Shape a data set using the Data Refinery according to specific observations Data modeling
- Differentiate the Watson Studio tools to create models
- Create a Watson Machine Learning model using AutoAI
- Create a Machine Learning model using SPSS Modeler
- Build a model using SparkML Modeler Flow Data science with notebooks
- Experiment with Jupyter notebooks
- Load from a file and run a Jupyter notebook with Watson Studio Model deployment
- Identify the model repository
- List model deployment and test options
- Deploy a model
- Test a deployed model
Dates & Locations
Exam & Certification
This course is not associted with an Certifcation
Training & Certification Guide
Frequently Asked Questions
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