Deploy a predictive machine learning model using IBM Cloud

от партнера
Coursera Project Network
В этом Проект с консультациями вы:

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
IntermediateУчащийся среднего уровня
CloudЗагрузка не требуется
VideoВидео на разделенном экране
Comment DotsАнглийский
LaptopТолько для ПК

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.

Навыки, которые вы получите

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

Будете учиться пошагово

На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:

  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.

Как устроены проекты с консультациями

Ваше рабочее пространство — это облачный рабочий стол в браузере. Ничего не нужно загружать.

На разделенном экране видео преподаватель предоставляет пошаговые

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