Artificial Intelligence (AI), and Machine Learning (ML), are two of the most recognizable technological terms of the twenty-first century. While they were initially used by researchers, large corporations now use them to solve business problems. Before cloud computing, machine learning models required a lot more computing resources and configurations. However, cloud providers like AWS, Microsoft Azure, Google Cloud Platform offer a lot more virtual resources for ML modeling. Although they offer a variety of managed low-code and no-code solutions to business problems, many scenarios require custom training and tuning of machine-learning models. This may require a lot more computing power.
AWS offers SageMaker services which include Machine Learning services for corporate applications. This allows for a flexible, hosted and configurable ML development environment. Although SageMaker isn’t new to the cloud, AWS took it one step further by creating The SageMaker Studio, which is a Cloud IDE that houses all SageMaker services under one roof.
1. The Studio
AWS SageMaker Studio, a unique service that provides multiple services under one UI, aids in the process of ML modeling building and fine-tuning parameters. First, you need to create a Domain in order to start a SageMaker Studio. A SageMaker Domain is composed of an Elastic file system (EFS), along with a list authorized users, security features, apps, and VPC configurations that protect the environment. You can create a domain with either SSO Authentication (or IAM) roles.

2. The User Interface
Once the domain is ready, the studio environment can be launched for a specific user. Multiple users can share a single environment. SageMaker Studio is like an IDE running in a cloud. It is responsive, flexible, functional, and allows us to change the environment in a flash of an eye. The studio has a quick launcher, which allows you to launch a Jupyter notebook or python terminal in one click.
AWS Sagemaker Studio is made up of four main parts:a. Left Sidebar
The left sidebar contains several icons that allow us to quickly switch between the resources. It contains icons for:
File Browser: Displays all files and resources that are open in the main window.
Git tools: Provides Git integration for the studio.
Running terminals and Kernels – Displays the current instances and kernels in the session.
Commands: Controls the studio and the notebooks that are being used during the session.
Notebook tools: Provides metadata about a notebook instance.
Tabs open: The main window shows the list of tabs that are open.
Jumpstart: One-Click, One-Click ML Solutions, Endpoints and Training Jobs on Various Scenarios
SageMaker components, registries: It’s a customizable pane. It can be used to view projects, jobs, pipelines and experiments, as well as data wrangler and data wrangler.
b. Right SideBar
The settings pellet on the right sidebar displays the current resource settings.
c. File and Resources Browser:
The File and Resource browser displays a list of available resources in the main area. It allows us to launch new notebooks, upload files and create folders.
d. Main Work Area
The main work area displays the resource in a large pane. It displays notebooks, terminals, and features such as data wrangler and feature store. We can also work on it.
3. Studio Notebooks
SageMaker Studio redesigned SageMaker notebooks to look like the new Studio Notebooks. Studio notebooks look and function very much like Jupyter notebooks, but with additional configurations and looks.

The above image shows the UI for a SageMaker Studio Notebook. The environment of the studio notebook can be customized by changing the EC2 instance type and SageMaker Image. We also have the option to change the Kernel that runs the code. The up