Sagemaker Studio (BETA)
The Amorphic platform provides integration with AWS SageMaker Studio to accelerate machine learning workflows in SageMaker.
Amazon SageMaker Studio is an integrated development environment(IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, and deploy models to production without leaving SageMaker Studio. It allows you to quickly switch environments and collaborate seamlessly within your organization to build ML models at scale.
Utilizing SageMaker Studio through Amorphic enables users to streamline their workflow by alleviating the burden of creating numerous configurations. By leveraging Amorphic, individuals can harness the complete capabilities of AWS SageMaker and Notebooks, facilitating advanced development of machine learning models and pipelines.
This is currently a beta feature and is currently only supported through API.
Studio Operations
AmorphicStudio provides the below operations.
| Operation | Description |
|---|---|
| Create Studio | Create a studio domain and space in AWS Sagemaker. |
| Update Studio | Update the attributes of studio. |
| Update Resource Access | Update dataset permissions for studio |
| Delete Studio | Delete studio components |
Sharing studios with users and groups is currently not supported.
How to Create a Sagemaker Studio ?
To create a Studio:
- Click on
+ New Studio - Fill in the details shown in the table:
| Attribute | Description |
|---|---|
| Studio Name | Unique name for Studio. |
| Description | Describe the studio's purpose and important details. |
| Volume Size in GB | Storage volume size in GB. |
| Datasets Access | Select datasets with write or read access required for the studio. |
| Direct Internet Access | Allow studio access internet. |
| Parameter Access | Select SSM parameters that can be used inside studio environment. |
| InstanceList | List of instances studio has permission to use inside the IDE while creating resources inside it. |
To enable data wrangler in Sagemkaer studio, user should create or update the studio instance list to include ml.m5.4xlarge instance type.
The data wrangler will have access to the following s3 path s3://<ML_TEMP_BUCKET>/sagemaker-studio/<STUDIO_ID>/ to store the processed data and the flow files.
Studio Details
When a new studio is created, Amorphic creates an AWS sagemaker domain, user-profile and space.
- Users will be able to see two urls on the details page which can be used to launch a Sagemaker Studio IDE - User Profile URL and Space URL
- The User Profile IDE is private to a user and Space IDE is a collaborative IDE which is be shared by all users having access to the studio
Studio Update Access to resources
Users can attach Amorphic datasets in readonly or write mode to studio to get access to them inside IDE. Parameters can also be updated in this manner.
Studio by default allows all type of instances while creating an app inside it. Users can provide a list of machine instances to access inside studio IDE from Amorphic UI to avoid accidental creation of high costing instances.
Studio Benefits
Amazon SageMaker Studio offers a unified experience for ML development. ML teams can perform the complete ML workflow in a single web-based visual interface.
Access to pretrained ML models, built-in algorithms, and prebuilt ML solutions
Studio Usecases
Create shared spaces in SageMaker Studio where your teams can read, edit, and run notebooks together in real time to streamline collaboration and communication. Teammates can review results together to immediately understand how a model performs without passing information back and forth.
Unify your end-to-end ML development in SageMaker Studio with the most comprehensive ML tools all in one place. SageMaker offers high-performing MLOps tools to help you automate and standardize ML workflows and governance tools to support transparency and auditability across your organization.
Build foundation models faster in SageMaker Studio with access to a wide range of publicly available models, notebooks backed by high performance compute for fine-tuning, and ability to scale to distributed training directly from Studio notebooks.
SageMaker Studio offers a unified experience to perform all data analytics and ML workflows. Create, browse, and connect to Amazon EMR clusters. Build, test, and run interactive data preparation and analytics applications with Amazon Glue interactive sessions. Monitor and debug Spark jobs using familiar tools such as Spark UI – all right from SageMaker Studio notebooks.