Machine-Learning-Models-with-AUTO-AI

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Machine-Learning-Models-with-AUTO-AI

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In this workshop you will learn how to build and deploy your own AI Models

Machine-Learning-Models-with-AUTO-AI 최신버젼 다운로드

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# Machine Learning Models with AUTO AI

In this workshop you will learn how to build and deploy your own AI Models.

For the workshop we will be using AutoAI, a graphical tool that analyses your dataset and discovers data transformations, algorithms, and parameter settings that work best for your problem setting.

Using AutoAI, you can build and deploy a machine learning model with sophisticated training features and no coding.

We will use some public datasets to build and deploy two different model pipelines, and analyse each of these models.

## Set up

## IBM Cloud

- https://ibm.biz/BdfKeq Use this link to sign up for an IBM Cloud account

- When you are signed up click `Create Resource` at the top of the Resources page. You can find the resources under the hamburger menu at the top left:

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/Create_resource.png)
 
- Search for Watson Studio and click on the tile:

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/Watson_Studio.png)

- Select the Lite plan and click `Create`.
- Go back to the Resources list and click on your Watson Studio service and then click `Get Started`.

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/launch.png)

## IBM Watson Studio

### Create a new Project

- You should now be in Watson Studio.
- Create a new project by clicking on `Get Started` and `New Project`, or `Create Project`

![](https://github.com/YaminiRao/Data-Visualisation-with-Python/blob/master/Images/Watson_Studio.png)

- Give your Project a name.
- Select an Object Storage from the drop-down menu or create a new one for free. This is used to store the notebooks and data. **Do not forget to click refresh when returning to the Project page.**

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/COS.png)

- click `Create`.

### Once you are in the Project Dashboard, click on "Add to Project" on the top right and select AutoAI Experiment

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/AutoAI.png)

### Associate a Machine Learning service

- Give your Auto AI experiment a unique name 
- Associate a Watson Machine Learning service, if you have already created one this will apear in the dropdown or you can create a new one. 
- Once this is done, click the "Reload" button for your Machine Learning service to appear

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/MLservice.png)

- Your machine learning service will appear under "Associated services"
- Click Create

### Upload your Data Sets

- In this workshop we will be analysing two Datasets 
- One to create a Classification model and the other to create a Regression Model

- Browse and add your Data source 
- Datasets for this workshop have been acquired from :

https://www.kaggle.com/noordeen/insurance-premium-prediction

https://github.com/IBM/predictive-model-on-watson-ml (with minor changes to original data)

https://www.kaggle.com/vikrishnan/boston-house-prices (with minor changes to original data)

https://developer.ibm.com/tutorials/generate-machine-learning-model-pipelines-to-choose-the-best-model-for-your-problem-autoai/

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/Data_Source.png)

## We will now work through the different aspects of configuring our AutoAI experiment and discuss the Classification Models that are created.

## Deploying you Model

- Once you select your final model pipeline. You can choose to Save it as a notebook or an ML model.

- Choose your option and Click Save

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/MLModel.png)

- Once your model is saved you will see a popup to View in Project or you can access the saved model from the Assets tab.

- Click on the saved model and Promote to a deployment space

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/PromoteSpace1.png)

- Select the target deployment space you want to use for this deployment. If you do not have one yet, you can create a new space.

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/PromoteSpace2.png)

- Access the deployment space from the left navigation pane.

- In your Deployment Space, Choose the model you want to deploy from the Assets tab and click the deploy icon as shown

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/Deploy.png)

- Choose your Deployment type and create your Deployment.

![](https://raw.githubusercontent.com/IBMDeveloperUK/Machine-Learning-Models-with-AUTO-AI/master/Images/Deploy_type.png)

Once your model is successuly deployed, you can access the API reference to make API calls or Test your model by providing input using a form or in a JSON format

## Part 2

## [Running the Web Apllication](WebApp.md)

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