# Build your AI model from scratch on ForePaaS

!> This tutorial uses the **Machine Learning Manager**, a service of the ForePaaS Legacy Platform that is not available on OVHcloud Data Platform. See [the current tutorials](/en/getting-further/index).

If you’ve been hearing about how Artificial Intelligence (AI) can help you make better business decisions and want to add more intelligence and machine learning to the data you have on hand, you're in the right place! 

In this tutorial, you'll learn the basics about the ForePaas Platform Artificial Intelligence features. You'll create, train and deploy your own AI model **in less than 30 minutes**.

⛳️ The goal is to get you up to speed on how to use the AI features of the ForePaas Platform so that you can start operationalizing AI asap. 

📥 No need to bring your own data or model, we provide them throughout the tutorial to accelerate the process. 

?> If you have never used the ForePaaS Platform before, we suggest you have a look at the first 3 parts of our Getting Started guide to learn how your [Connectors](en/getting-started/app-init/dc), [Lakehouse Manager](en/getting-started/app-init/lhm) and [Data Processing Engine](en/getting-started/app-init/dpe) work.

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## AI in production in five easy steps

Generally speaking, the creation and deployment of your machine learning model will be completed in 5 major steps:

<div class="Project-step">
   <div class="step">1</div>
   <a class="landing-link" href="#/en/getting-started/ml/dataset.md">
      <img data-no-zoom src="en/product/ml/picts/dataset-icon.png" alt="Dataset" style="width:100px;height:auto;"/>
      <div class="text">
         <h2>Data preparation</h2>
         <p>Specify your train and test datasets.</p>
      </div>
   </a>
</div>
<div class="Project-step">
   <div class="step">2</div>
   <a class="landing-link" href="#/en/getting-started/ml/training.md">
      <img data-no-zoom src="en/product/ml/picts/training-icon.png" alt="Training" style="width:100px;height:auto;"/>
      <div class="text">
         <h2>Training procedure</h2>
         <p>Choose the algorithm at the core of your model.</p>
      </div>
   </a>
</div>
<div class="Project-step">
   <div class="step">3</div>
   <a class="landing-link" href="#/en/getting-started/ml/tuning.md">
      <img data-no-zoom src="en/product/ml/picts/tuning-icon.png" alt="Tuning" style="width:100px;height:auto;"/>
      <div class="text">
         <h2>Hyper-parameter tuning</h2>
         <p>Fine-tune the hyper-parameters of your estimator using our insightful studio.</p>
      </div>
   </a>
</div>
<div class="Project-step">
   <div class="step">4</div>
   <a class="landing-link" href="#/en/getting-started/ml/validation.md">
      <img data-no-zoom src="en/product/ml/picts/validation-icon.png" alt="Validation" style="width:100px;height:auto;"/>
      <div class="text">
         <h2>Model selection</h2>
         <p>Compare trained models and pick the one that suits your needs.</p>
      </div>
   </a>
</div>
<div class="Project-step">
   <div class="step">5</div>
   <a class="landing-link" href="#/en/getting-started/ml/deployment.md">
      <img data-no-zoom src="en/product/ml/picts/deployment-icon.png" alt="Deployment" style="width:100px;height:auto;"/>
      <div class="text">
         <h2>Deployment settings</h2>
         <p>Specify the settings for your production-grade inference service</p>
      </div>
   </a>
</div>

?>💡 **Good news!**   
The Machine Learning Manager, as in the rest of the platform, does a lot of the work for you automatically, from the configuration to the deployment and scalability. This will help you focus on the logic of your AI model rather than the technical administration. ⭐