PyCaret is an open-source, low-code machine learning library in Python that streamlines the process of building, training, and deploying machine learning models. It is designed to simplify complex machine learning workflows by providing an intuitive and user-friendly interface, making it an excellent choice for both beginners and experienced data scientists. With PyCaret, users can efficiently perform data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation with minimal coding effort. This significantly reduces the time required for iterative experimentation, allowing for rapid prototyping and deployment of machine learning models.

One of PyCaret’s standout features is its automation capabilities, enabling users to compare multiple machine learning models with just a few lines of code and select the best-performing one based on various performance metrics. It supports a wide range of machine learning algorithms, including classification, regression, clustering, anomaly detection, and natural language processing (NLP). Additionally, PyCaret seamlessly integrates with popular machine learning frameworks such as scikit-learn, XGBoost, LightGBM, and CatBoost, making it highly versatile for different use cases.

Beyond model training, PyCaret offers advanced functionalities such as model interpretability, ensemble learning, automatic hyperparameter optimization, and integration with cloud platforms for model deployment. It also provides built-in visualization tools to help users understand data patterns, feature importance, and model performance. Moreover, PyCaret supports deployment into production environments with ease, allowing models to be exported and used in web applications, dashboards, or cloud services.

PyCaret is widely adopted in industries such as finance, healthcare, retail, and marketing, where quick experimentation and deployment of machine learning models are crucial. By reducing the technical complexity traditionally associated with machine learning, PyCaret empowers data professionals, business analysts, and software developers to harness the power of machine learning without requiring deep expertise in coding or algorithm development. With its extensive feature set and ease of use, PyCaret is a valuable tool for accelerating machine learning projects and making AI more accessible to a broader audience.

To subscribe to this product from Azure Marketplace and initiate an instance using the Azure compute service, follow these steps:

1. Navigate to Azure Marketplace and subscribe to the desired product.
2. Search for “virtual machines” and select “Virtual machines” under Services.
3. Click on “Add” in the Virtual machines page, which will lead you to the Create a virtual machine page.
4. In the Basics tab:

• • Ensure the correct subscription is chosen under Project details.
  • Opt for creating a new resource group by selecting “Create new resource group” and name it as “myResourceGroup.”

5. Under Instance details:

• • Enter “myVM” as the Virtual machine name.
  • Choose “East US” as the Region.
  • Select “Ubuntu 18.04 LTS” as the Image.
  • Leave other settings as default.

6. For Administrator account:

• • Pick “SSH public key.”
  • Provide your user name and paste your public key, ensuring no leading or trailing white spaces.

7. Under Inbound port rules > Public inbound ports:

• • Choose “Allow selected ports.”
  • Select “SSH (22)” and “HTTP (80)” from the drop-down.

8. Keep the remaining settings at their defaults and click on “Review + create” at the bottom of the page.
9. The “Create a virtual machine” page will display the details of the VM you’re about to create. Once ready, click on “Create.”
10. The deployment process will take a few minutes. Once it’s finished, proceed to the next section.

To connect to the virtual machine:

1. Access the overview page of your VM and click on “Connect.”
2. On the “Connect to virtual machine” page:

• • Keep the default options for connecting via IP address over port 22.
  • A connection command for logging in will be displayed. Click the button to copy the command. Here’s an example of what the SSH connection command looks like:
    “`
    ssh azureuser@10.111.12.123
    “`

3. Using the same bash shell that you used to generate your SSH key pair, you can either reopen the Cloud Shell by selecting >_ again

or going to https://shell.azure.com/bash.
4. Paste the SSH connection command into the shell to initiate an SSH session.

Usage/Deployment Instructions

Anarion Technologies – Pycaret

Note: Search product on Azure marketplace and click on “Get it now”

Click on Continue

Click on Create

Creating a virtual machine, enter or select appropriate values for zone, machine type, resource group and so on as per your choice.

After Process of Create Virtual
Machine. You have got an Option Go to
Resource Group

Click on the Network
Security Group: zenml-nsg

Click on Inbound
Security Rule

Click on Add

Add Port

Add Port

Destination
Port Ranges Section*  (where default
value is 8080)

8888

Select Protocol as
TCP

Option Action is
to be Allow

Click on Add     

Click on Refresh

Click Go to Resource Group

Copy the Public IP Address

SSH into Terminal and Run these Commands:

$ sudo su
$ cd ../..
$ apt update

$ source pycaret-env/bin/activate

$ python –version

$ python

Thanks!!!