IBM Course

course overview

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The focus of this course is on the tools and services available in IBM Watson Studio that can be used to build, test, and deploy machine learning models on Cloud Pak for Data V4.8. It takes the data scientist or business analyst on a journey from the creation of several machine learning models to its deployment and testing. Various tools and services, as well as programming and graphical user interfaces, are used in the process. The course ends with the sharing of assets on GitHub, and a brief discussion on governance and stewardship.


The course is designed for Data Scientists and Business Analysts. It caters to aspiring or practicing Data Scientists, and Business Analysts with prior knowledge of Data Science and Machine Learning, but lacking familiarity with IBM tools and IBM Watson. Rather than teaching data science and machine learning, the course focuses on demonstrating how IBM Watson tools and services can address significant business challenges, targeting professionals at the associate level and beyond.

Skills Gained

  • Define a solution to a business problem using tools and frameworks from IBM Watson Studio
  • Demonstrate how the AI lifecycle can be automated by building a rapid prototype using AutoAI
  • Build, train, and deploy a machine learning model with the tools and services available in Watson Studio
  • Implement GitHub Integration and team collaboration in Watson Studio


Before taking this course, you should have:

  • Knowledge of Data Science
  • Knowledge of Machine Learning
  • Experience with the Python programming language



  • Discuss the four steps that make up the AI Ladder
  • Explain the AI Lifecycle and the different personas that are involved in an AI project
  • Explain the components that comprise a Watson Studio project

Rapid prototyping with AutoAI

  • Describe the benefits of AutoAI for rapid prototyping
  • Discuss the steps needed to run a successful AutoAI experiment
  • Interpret and discuss the results from an AutoAI experiment

Creating, testing, and deploying machine learning models

  • Discuss various ways of building a machine learning model in Watson Studio by using programming and visual interfaces
  • Discuss the contents of the model repository
  • Describe the various deployment methods for AI models

Governance, integration, and collaboration

  • Describe the capabilities and features to support governance of your data
  • Describe the team collaboration features of Watson Studio

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Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. Its a cost effective option. One on one training can be delivered too, at reasonable rates.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

All $ prices are in USD unless it’s a NZ or AU date

SPVC = Self Paced Virtual Class

LVC = Live Virtual Class

Please Note: All courses are availaible as Live Virtual Classes

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