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Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio W7L149G

  • CMDBID 1001250
  • Course Code W7L149G
  • Duration 1 Days
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IBM Analytics Course

course overview

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Overview

This course 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.

Audience

Data Scientists and Business Analysts

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

Prerequisites

The data scientists’ and business analysts’ prerequisite skills and knowledge include:

  • Experience working in a browser
  • Some experience writing and running Python programs
  • Some experience with the Jupyter Notebook environment
  • Some experience working with a graphical user interface (GUI)
  • Knowledge of machine learning (regression, decision trees, and random forests)
  • Some experience working with GitHub

Outline

  • Introduction
  • Rapid prototyping with AutoAI
  • Creating, testing, and deploying machine learning models
  • Governance, integration, and collaboration

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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.

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SPVC = Self Paced Virtual Class

LVC = Live Virtual Class

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