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0.6/get-started/create_lab/index.html

@@ -1135,46 +1135,49 @@ <h1 id="how-to-create-your-first-lab-environment">How to create your first Lab e
 combined with already loved tools such as scikit-learn, numpy and pandas.
 To create your first <strong>Lab</strong>, you can use the <strong>“Create Lab”</strong> from Fabric’s home, or you can access it from the Labs
 module by selecting it on the left side menu, and clicking the <strong>“Create Lab”</strong> button.</p>
-<p><img alt="Create dataset with upload csv" src="../../assets/quickstart/create_lab/create_lab.webp" style="width:75%" /></p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <p>
-        <img src="/assets/quickstart/create_lab/create_lab.webp" alt="Select create a lab from Home" style="width: 75%;">
-    </p>
-</div>
-
+<p><img alt="Select create a lab from Home" src="../../assets/quickstart/create_lab/create_lab.webp" style="width:75%" /></p>
 <p>Next, a menu with different IDEs will be shown. As a quickstart select <em>Jupyter Lab</em>. As labs are development environments
 you will be also asked what language you would prefer your environment to support: <em>R</em> or <em>Python</em>. Select Python.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_lab/select_ide.webp" alt="Select an IDE" style="width: 50%;">
-    <img src="/assets/quickstart/create_lab/select_language.webp" alt="Python or R" style="width: 50%;">
-</div>
-
+<table>
+<thead>
+<tr>
+<th>Select IDE</th>
+<th>Select language</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><img alt="Select an IDE" src="../../assets/quickstart/create_lab/select_ide.webp" style="width:90%" /></td>
+<td><img alt="Python or R" src="../../assets/quickstart/create_lab/select_language.webp" style="width:90%" /></td>
+</tr>
+</tbody>
+</table>
 <p>Bundles are environments with pre-installed packages. Select YData bundle, so we can leverage some other Fabric features
 such as Data Profiling, Synthetic Data and Pipelines.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_lab/select_bundle.webp" alt="Select the bundle for development" style="width: 75%;">
-</div>
-
+<p><img alt="Select the bundle for development" src="../../assets/quickstart/create_lab/select_bundle.webp" style="width:75%" /></p>
 <p>As a last step, you will be asked to configure the infrastructure resources for this new environment as well as giving it
 a <em>Display Name</em>. We will keep the defaults,
 but you have flexibility to select GPU acceleration or whether you need more computational resources for your developments.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_lab/select_infrastructure.webp" alt="Select the computational resources" style="width: 75%;">
-</div>
-
+<p><img alt="Select the computational resources" src="../../assets/quickstart/create_lab/select_infrastructure.webp" style="width:75%" /></p>
 <p>Finally, your Lab will be created and added to the "Labs" list, as per the image below. The status of the lab will be
 🟡 while preparing, and this process takes a few minutes, as the infrastructure is being allocated to your development environment.
 As soon as the status changes to 🟢, you can open your lab by clicking in the button as shown below:</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_lab/open_lab.webp" alt="Open your lab environment" style="width: 75%;">
-</div>
-
+<p><img alt="Open your lab environment" src="../../assets/quickstart/create_lab/open_lab.webp" style="width:75%" /></p>
 <p>Create a new notebook in the JupyterLab and give it a name. You are now ready to start your developments!</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_lab/notebook_creation.webp" alt="Create a new notebook" style="width: 50%;">
-    <img src="/assets/quickstart/create_lab/notebook_created.webp" alt="Notebook created" style="width: 50%;">
-</div>
-
+<table>
+<thead>
+<tr>
+<th>Create a new notebook</th>
+<th>Notebook created</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><img alt="Create a new notebook" src="../../assets/quickstart/create_lab/notebook_creation.webp" style="width:90%" /></td>
+<td><img alt="Notebook created" src="../../assets/quickstart/create_lab/notebook_created.webp" style="width:90%" /></td>
+</tr>
+</tbody>
+</table>
 <p><strong>Congrats!</strong> 🚀 You have now successfully created your first <strong>Lab</strong> a code environment, so you can benefit from the most
 advanced Fabric features as well as compose complex data workflows.
 Get ready for your journey of improved quality data for AI.</p>

0.6/get-started/create_pipeline/index.html

@@ -1141,86 +1141,80 @@ <h1 id="how-to-create-your-first-pipeline">How to create your first Pipeline</h1
 or even publishing a model to production environments.</p>
 <p>In this tutorial we will build a simple and generic pipeline that use a <strong>Dataset</strong> from Fabric's <strong>Data Catalog</strong> and profile to check it's quality.
 We have the notebooks template already available. For that you need to access the <em>"Academy"</em> folder as per the image below.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/academy_folder.webp" alt="Academy folder" style="width: 75%;">
-</div>
-
+<p><img alt="Academy folder" src="../../assets/quickstart/create_pipeline/academy_folder.webp" style="width:75%" /></p>
 <p>Make sure to copy all the files in the folder "3 - Pipelines/quickstart" to the root folder of your lab, as per the image below.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/copy_files.webp" alt="Select your pipeline editor" style="width: 75%;">
-</div>
-
+<p><img alt="Select your pipeline editor" src="../../assets/quickstart/create_pipeline/copy_files.webp" style="width:75%" /></p>
 <p>Now that we have our notebooks we need to make a small change in the notebook "1. Read dataset". Go back to your <strong>Data Catalog</strong>, from one of the datasets
 in your Catalog list, select the three vertical dots and click in <strong>"Explore in Labs"</strong> as shown in the image below.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/explore_in_labs.webp" alt="Explore the dataset in the labs" style="width: 70%;">
-</div>
-
+<p><img alt="Explore the dataset in the labs" src="../../assets/quickstart/create_pipeline/explore_in_labs.webp" style="width:75%" /></p>
 <p>The following screen will be shown. Click in copy.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/code_snippet.webp" alt="Dataset code snippet" style="width: 35%;">
-</div>
-
+<p><img alt="Dataset code snippet" src="../../assets/quickstart/create_pipeline/code_snippet.webp" style="width:35%" /></p>
 <p>Now that we have copied the code, let's get back to our <strong>"1. Read data.ipynb"</strong> notebook, and replace the first code cell by with the new code. This will allow us to use a
 dataset from the Data Catalog in our pipeline.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/og_code.webp" alt="Dataset code snippet" style="width: 50%;">
-    <img src="/assets/quickstart/create_pipeline/replaced_code.webp" alt="Dataset code snippet" style="width: 50%;">
-</div>
-
+<table>
+<thead>
+<tr>
+<th>Placeholder code</th>
+<th>Replaced with code snippet</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><img alt="Select an IDE" src="../../assets/quickstart/create_pipeline/og_code.webp" /></td>
+<td><img alt="Python or R" src="../../assets/quickstart/create_pipeline/replaced_code.webp" /></td>
+</tr>
+</tbody>
+</table>
 <p>With our notebooks ready, we can now configure our <strong>Pipeline</strong>.
 For this quickstart we will be leveraging an already existing pipeline - double-click the file <em>my_first_pipeline.pipeline</em>. You should see a pipeline
 as depicted in the images below.
 To create a new Pipeline, you can open the lab launcher tab and select <strong>"Pipeline Editor"</strong>.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/open_pipeline.webp" alt="Open pipeline" style="width: 40%;">
-    <img src="/assets/quickstart/create_pipeline/my_first_pipeline.webp" alt="My first pipeline" style="width: 60%;">
-</div>
-
+<table>
+<thead>
+<tr>
+<th>Open Pipeline</th>
+<th>My first pipeline</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><img alt="Open pipeline" src="../../assets/quickstart/create_pipeline/open_pipeline.webp" /></td>
+<td><img alt="Python or R" src="../../assets/quickstart/create_pipeline/my_first_pipeline.webp" /></td>
+</tr>
+</tbody>
+</table>
 <p>Before running the pipeline, we need to check each component/step properties and configurations. Right-click each one of the steps, select <em>"Open Properties"</em>, and a
 menu will be depicted in your right side. Make sure that you have <em>"YData - CPU"</em> selected as the <strong>Runtime Image</strong> as show below.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/open_properties.webp" alt="Open pipeline" style="width: 50%;">
-    <img src="/assets/quickstart/create_pipeline/runtime_image.webp" alt="My first pipeline" style="width: 50%;">
-</div>
-
+<table>
+<thead>
+<tr>
+<th>Open properties</th>
+<th>Runtime image</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><img alt="Open pipeline" src="../../assets/quickstart/create_pipeline/open_properties.webp" /></td>
+<td><img alt="Python or R" src="../../assets/quickstart/create_pipeline/runtime_image.webp" /></td>
+</tr>
+</tbody>
+</table>
 <p>We are now ready to create and run our first pipeline. In the top left corner of the pipeline editor, the run button
 will be available for you to click.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/run_pipeline.webp" alt="Select your pipeline editor" style="width: 75%;">
-</div>
-
+<p><img alt="Select your pipeline editor" src="../../assets/quickstart/create_pipeline/run_pipeline.webp" style="width:75%" /></p>
 <p>Accept the default values shown in the run dialog and start the run</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/pipeline_default_dialog.webp" alt="Pipeline configuration confirm dialog" style="width: 30%;">
-</div>
-
+<p><img alt="Pipeline configuration confirm dialog" src="../../assets/quickstart/create_pipeline/pipeline_default_dialog.webp" style="width:35%" /></p>
 <p>If the following message is shown, it means that you have create a run of your first pipeline.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/pipeline_creation_success.webp" alt="Select your pipeline editor" style="width: 60%;">
-</div>
-
+<p><img alt="Select your pipeline editor" src="../../assets/quickstart/create_pipeline/pipeline_creation_success.webp" style="width:60%" /></p>
 <p>Now that you have created your first pipeline, you can select the <strong>Pipeline</strong> from Fabric's left side menu.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/pipelines_menu.webp" alt="Select Fabric Pipelines" style="width: 70%;">
-</div>
-
+<p><img alt="Select Fabric Pipelines" src="../../assets/quickstart/create_pipeline/pipelines_menu.webp" style="width:75%" /></p>
 <p>Your most recent pipeline will be listed, as shown in below image.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/my_pipeline_record.webp" alt="My first pipeline listed" style="width: 70%;">
-</div>
-
+<p><img alt="My first pipeline listed" src="../../assets/quickstart/create_pipeline/my_pipeline_record.webp" style="width:75%" /></p>
 <p>To check the run of your pipeline, jump into the <strong>"Run"</strong> tab. You will be able to see your first pipeline running!</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/my_first_pipeline_run.webp" alt="My first pipeline listed" style="width: 70%;">
-</div>
-
+<p><img alt="Run pipeline" src="../../assets/quickstart/create_pipeline/my_first_pipeline_run.webp" style="width:75%" /></p>
 <p>By clicking on top of the record you will be able to see the progress of the run step-by-step, and visualize the outputs of each and every
 step by clicking on each step and selecting the <strong>Visualizations</strong> tab.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/create_pipeline/pipeline_progress.webp" alt="My first pipeline listed" style="width: 70%;">
-</div>
-
+<p><img alt="Run pipeline" src="../../assets/quickstart/create_pipeline/pipeline_progress.webp" style="width:70%" /></p>
 <p><strong>Congrats!</strong> 🚀 You have now successfully created your first <strong>Pipeline</strong> a code environment, so you can benefit from Fabric's
 orchestration engine to crate scalable, versionable and comparable data workflows.
 Get ready for your journey of improved quality data for AI.</p>

0.6/get-started/create_syntheticdata_generator/index.html

@@ -1138,10 +1138,7 @@ <h1 id="how-to-create-your-first-synthetic-data-generator">How to create your fi
 <p>With your first dataset created, you are now able to start the creation of your Synthetic Data generator. You can either
 select <strong>"Synthetic Data"</strong> from your left side menu, or you can select <strong>"Create Synthetic Data"</strong> in your project Home
 as shown in the image below.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/synthetic_data/create_synthetic_data.webp" alt="Create Synthetic Data" style="width: 75%;">
-</div>
-
+<p><img alt="Create Synthetic Data" src="../../assets/quickstart/synthetic_data/create_synthetic_data.webp" style="width:75%" /></p>
 <p>You'll be asked to select the dataset you wish to generate synthetic data from and verify the columns you'd like to
 include in the synthesis process, validating their <em>Variable</em> and <em>Data Types</em>.</p>
 <div class="admonition tip">
@@ -1151,42 +1148,24 @@ <h1 id="how-to-create-your-first-synthetic-data-generator">How to create your fi
 to consider it a String, under the light of a dataset where "Name" refers to the name of the product purchases, it might be more
 beneficial to set it as a Category.</p>
 </div>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/synthetic_data/synthetic_data_columns_sel.webp" alt="Configure Metadata" style="width: 75%;">
-</div>
-
+<p><img alt="Configure Metadata" src="../../assets/quickstart/synthetic_data/synthetic_data_columns_sel.webp" style="width:75%" /></p>
 <p>Finally, as the last step of our process it comes the <strong>Synthetic Data</strong> specific configurations, for this particular case we
 only need to define a <em>Display Name,</em> and we can finish the process by clicking in the <strong>"Save"</strong> button as per the image below.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/synthetic_data/synthetic_data_configuration.webp" alt="Save Synthetic Data configurations" style="width: 75%;">
-</div>
-
+<p><img alt="Save Synthetic Data configurations" src="../../assets/quickstart/synthetic_data/synthetic_data_configuration.webp" style="width:75%" /></p>
 <p>Your <strong>Synthetic Data</strong> generator is now training and listed under <strong>"Synthetic Data"</strong>. While the model is being trained, the <em>Status</em> will be
 🟡, as soon as the training is completed successfully it will transition to 🟢 as per the image below.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/synthetic_data/trained_synthetic_data.webp" alt="Synthetic data generator trained successfully" style="width: 75%;">
-</div>
-
+<p><img alt="Synthetic data generator trained successfully" src="../../assets/quickstart/synthetic_data/trained_synthetic_data.webp" style="width:75%" /></p>
 <p>Once the Synthetic Data generator has finished training, you're ready to start generating your first synthetic dataset.
 You can start by exploring an overview of the model configurations and even download a PDF report with a comprehensive overview of your
 Synthetic Data Quality Metrics. Next, you can generate synthetic data samples by accessing the <em>Generation</em> tab or click on <em>"Go to Generation"</em>.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/synthetic_data/synthetic_data_overview.webp" alt="Synthetic data generator overview" style="width: 75%;">
-</div>
-
+<p><img alt="Synthetic data generator overview" src="../../assets/quickstart/synthetic_data/synthetic_data_overview.webp" style="width:75%" /></p>
 <p>In this section, you are able to generate as many synthetic samples as you want.
 For that you need to define the number rows to generate and click <em>"Generate"</em>, as depicted in the image below.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/synthetic_data/set_generation.webp" alt="Generate synthetic data records" style="width: 75%;">
-</div>
-
+<p><img alt="Generate synthetic data records" src="../../assets/quickstart/synthetic_data/set_generation.webp" style="width:75%" /></p>
 <p>A new line in your <em>"Sample History"</em> will be shown and as soon as the sample generation is completed you will be able to
 <em>"Compare"</em> your synthetic data with the original data, add as a Dataset with <em>"Add to Data Catalog"</em> and last but not the least
 download it as a file with <em>"Download csv"</em>.</p>
-<div style="display: flex; justify-content: center;align-items: center;">
-    <img src="/assets/quickstart/synthetic_data/generated_synthetic_sample.webp" alt="Synthetic data generator trained" style="width: 75%;">
-</div>
-
+<p><img alt="Synthetic data generator trained" src="../../assets/quickstart/synthetic_data/generated_synthetic_sample.webp" style="width:75%" /></p>
 <p><strong>Congrats!</strong> 🚀 You have now successfully created your first <strong>Synthetic Data</strong> generator with Fabric.
 Get ready for your journey of improved quality data for AI.</p>