feat: Pybytes ML integration

config.toml

@@ -1408,12 +1408,19 @@ theme = "doc-theme"
     parent = "pybytes"
     weight = 80
 
- [[menu.main]]
-     name = "Pymesh Integration"
-     url = "/pybytes/pymeshintegration/"
-     identifier = "pybytes@pymeshintegration"
-     parent = "pybytes"
-     weight = 90
+[[menu.main]]
+   name = "Pymesh Integration"
+   url = "/pybytes/pymeshintegration/"
+   identifier = "pybytes@pymeshintegration"
+   parent = "pybytes"
+   weight = 90
+
+[[menu.main]]
+   name = "Machine Learning"
+   url = "/pybytes/mlintegration/"
+   identifier = "pybytes@mlintegration"
+   parent = "pybytes"
+   weight = 100
 
 [[menu.main]]
     name = "Amazon IoT"

content/pybytes/mlintegration/_index.md

@@ -0,0 +1,16 @@
+---
+title: "Machine Learning Integration"
+aliases:
+---
+
+## What does Machine Learning integration offer you?
+
+This documentation is a quick introduction to the new Machine Learning integration features on Pybytes.
+
+The Machine Learning integration is here to help you to create smart machine learning models using Pycom devices.
+
+## Let's get started!
+
+* [Machine Learning Model Creation](/pybytes/mlintegration/modelcreation)
+
+* [Machine Learning Features](/pybytes/mlintegration/features)

content/pybytes/mlintegration/features.md

@@ -0,0 +1,65 @@
+## Model Features
+
+### Data Acquisition
+
+#### Create Sample
+
+In the data acquisition module the samples for training can be collected.
+
+After setting all sample parameters in the form click on **Create Sample** to start.
+
+The sampling occurs when the device led is amber, when the light turns purple, the device is processing and saving the samples.
+
+If the device gets stuck showing the purple light, maybe the sampling should be done again.
+
+#### Data Collection
+
+The data collection shows the collected samples. Click on the sample to see the graph.
+
+![Data Acquisition](/gitbook/assets/pybytes/ml/data_acquisition_graph.png)
+
+### Processing
+
+#### Raw Data
+
+In the processing tab, the Windows Size and Windows Step should be filled. Select the data range on the graph to fill the Windows step.
+
+#### Spectral Analysis
+
+After selecting the data range to be analyzed. Fill the form and click in the button **Process Signal**
+
+![Processing](/gitbook/assets/pybytes/ml/processing.png)
+
+#### Training
+
+Fill the Neural Network Settings form and click on **START TRAINING** to train the model.
+
+The results can be checked on the Training Performance section.
+
+![Training](/gitbook/assets/pybytes/ml/training.png)
+
+#### Testing
+
+In the module, the samples for testing can be collected.
+
+After setting all sample parameters in the form click on **Create Sample** to start.
+
+#### Data Collection
+
+The data collection shows the testing collected samples. Click on the **Test xxx Samples** to test the module.
+
+![Testing](/gitbook/assets/pybytes/ml/testing.png)
+
+After the testing is performed, the results can be checked below the Data collection form.
+
+![Testing Results](/gitbook/assets/pybytes/ml/testing_results.png)
+
+#### Model Deployment
+
+After all training and testing, the model can be deployed into the devices.
+
+Select the devices and click on the **DEPLOY MODEL** button.
+
+![Model Deployment](/gitbook/assets/pybytes/ml/deploy.png)
+
+[**Machine Learning Integration**](/pybytes/mlintegration)

content/pybytes/mlintegration/modelcreation.md

@@ -0,0 +1,42 @@
+## Model Creation
+
+The first step to use the machine learning feature on Pybytes is the model creation.
+
+The Machine Learning app can be accessed in the main sidebar.
+
+![Machine Learning App](/gitbook/assets/pybytes/ml/pybytes_front_page.png)
+
+
+To create a new model, click on **ADD MODEL** button and follow the wizard.
+
+The existing models are listed below.
+
+![Create Model](/gitbook/assets/pybytes/ml/ml_page.png)
+
+### Model Wizard
+
+#### The first step is the model definition.
+
+In the model definition the fields **Name**, **Description**, and **Sample frequency** should be fulfilled.
+
+![Model Definition](/gitbook/assets/pybytes/ml/model_definition.png)
+
+#### The second step is the model configuration.
+
+In the model configuration, the processing block type and learning block technique should be selected.
+
+In the beta version, only the Spectral Analysis (processing block type) and Neural Network (learning block technique) are available.
+
+![Model Configuration](/gitbook/assets/pybytes/ml/model_config.png)
+
+#### The third step is the Device Selection.
+
+In the third step the devices that will be used to train and test the model should be selected.
+
+![Device Selection](/gitbook/assets/pybytes/ml/select_device.png)
+
+And now the model is ready to be used!
+
+![Device Selection](/gitbook/assets/pybytes/ml/model_created.png)
+
+Check this link to learn how to train and test the model [**Model features**](/pybytes/mlintegration/features)