Deploy a predictive machine learning model using IBM Cloud

von
Coursera Project Network
In diesem angeleitetes Projekt werden Sie:

Create, evaluate and deploy a machine learning model using Watson Studio (without writing a single line of code).

Deploy the model and try out as a web service frontend to make predictions.

Clock2 hours
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

In this 1-hour long project-based course, you will be able to create, evaluate and save a machine learning model (without writing a single line of code) using Watson Studio on IBM Cloud Platform, and you will make deployment of the model and try out as a web service frontend to make predictions. This guided project is for Data Scientists, Machine Learning Engineers, and Developers who want a way to deliver their machine learning code available to be integrated into an application and using it as a web service. We will do everything in a development mode without any costs using a free IBM Cloud account. To be successful in this project, you should be familiar with machine learning methodologies, like training, prediction, evaluation, and basic knowledge in some machine learning algorithms is appreciated too, so that way you will understand the results before making a deployment. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Kompetenzen, die Sie erwerben werden

  • Data Science
  • deployment
  • Machine Learning
  • Classification Algorithms
  • Machine Learning (ML) Algorithms

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  1. Introduction to the IBM Cloud and Watson Studio.

  2. Create a Project and Import our Data.

  3. Explore the Data Refinery and create a Machine Learning Service.

  4. Train, evaluate and save the Machine Learning model.

  5. Deploy and test the ML model as a Web Service.

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

Häufig gestellte Fragen

Häufig gestellte Fragen

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