Maximize Usage of VPC Mode with Zero Internet Connectivity using Amazon SageMaker JumpStart for Leveraging Generative AI Foundation Models
Introduction:
With recent advancements in generative AI, there is a growing interest in using this technology across various industries to solve specific business problems. Generative AI is a type of AI that has the ability to create new content and ideas, such as conversations, stories, images, videos, and music. Powered by large pre-trained models known as foundation models (FMs), generative AI can perform a wide range of tasks spanning multiple domains. However, organizations operating in heavily regulated spaces, like the financial services and healthcare industries, require the FM-based solutions to run in their own protected environments without internet access. This is where Amazon SageMaker JumpStart comes into play. It is an ML hub offering algorithms, models, and ML solutions, allowing ML practitioners to choose from a selection of open source FMs and deploy them in their own Virtual Private Clouds (VPCs). In this post, we will demonstrate how to deploy a Flan T5-XXL model in a VPC with no internet connectivity using SageMaker JumpStart. We will cover topics such as deploying a foundation model using SageMaker JumpStart in a VPC with no internet access, the advantages of deploying FMs via JumpStart in VPC mode, and alternate ways to customize the deployment of foundation models. This post will provide a step-by-step guide on how to set up a VPC, deploy SageMaker Studio, and deploy the Flan T5-XXL model using JumpStart within the VPC. This solution not only allows organizations to leverage the power of generative AI but also ensures that their environment is secure and compliant with their regulatory requirements.
Full Article: Maximize Usage of VPC Mode with Zero Internet Connectivity using Amazon SageMaker JumpStart for Leveraging Generative AI Foundation Models
Advantages of Deploying Generative AI with SageMaker JumpStart in a VPC with No Internet Access
In recent times, the utilization of generative AI has gained significant attention due to its ability to solve specific business problems in various industries. Generative AI refers to a type of AI that has the capability to create new content and ideas, such as conversations, stories, images, videos, and music. This technology is powered by large models known as foundation models (FMs), which have been pre-trained on extensive amounts of data. These FMs have the ability to perform a wide range of tasks across multiple domains, including writing blog posts, generating images, solving math problems, engaging in dialogues, and answering questions based on a document. Unlike traditional ML models that are designed for specific tasks like sentiment analysis, image classification, and trend forecasting, FMs are larger and more versatile.
Concerns with FM-Based Solutions in Protected Environments
Although organizations are eager to harness the power of FMs, they also have the need for these FM-based solutions to operate within their own secure environments. In heavily regulated industries such as global financial services, healthcare, and life sciences, there are stringent auditory and compliance requirements that necessitate running the AI solutions in Virtual Private Clouds (VPCs). In fact, these environments often restrict direct internet access to prevent exposure to any unauthorized traffic.
Introducing Amazon SageMaker JumpStart
To address these demands, Amazon SageMaker provides a solution called JumpStart. JumpStart is an ML hub that offers a wide range of algorithms, models, and ML solutions. With SageMaker JumpStart, ML practitioners have access to a growing list of best-performing open source foundation models. Additionally, JumpStart also enables the deployment of these models within a user’s own Virtual Private Cloud (VPC).
Deploying a Flan-T5 XXL Model in a VPC with No Internet Connectivity
In this article, we will illustrate how to use SageMaker JumpStart to deploy a Flan-T5 XXL model within a VPC with no internet connectivity. We will cover the following topics:
1. Deploying a foundation model using SageMaker JumpStart in a VPC with no internet access
2. The advantages of deploying foundation models via SageMaker JumpStart in VPC mode
3. Alternate ways to customize the deployment of foundation models via JumpStart
Solution Overview
The solution involves the following steps:
1. Setting up a VPC with no internet connection
2. Setting up Amazon SageMaker Studio using the created VPC
3. Deploying the Flan T5-XXL generative AI foundation model using JumpStart in the VPC with no internet access
Architecture Diagram
A visual representation of the solution’s architecture is provided in the article.
Prerequisites
To follow along with the implementation of this solution, you will need the following:
1. Set up a VPC with no internet connection by creating a CloudFormation stack using the provided 01_networking.yaml template. It creates a new VPC with two private subnets across two Availability Zones, ensuring no internet connectivity. Additionally, it deploys gateway VPC endpoints for accessing Amazon S3 and interface VPC endpoints for SageMaker and other services.
2. Set up Amazon SageMaker Studio using another CloudFormation stack with the provided 02_sagemaker_studio.yaml template. This stack creates a Studio domain, Studio user profile, and necessary IAM roles. Ensure to provide the name of the VPC stack created in the previous step as the CoreNetworkingStackName parameter.
Deploying the Generative AI Foundation Model Flan T5-XXL Using SageMaker JumpStart
The Flan T5-XXL model can be deployed either through SageMaker Studio or via API. For this article, we will demonstrate the deployment process using SageMaker Studio. Here are the steps:
1. In SageMaker Studio, choose JumpStart under the Prebuilt and Automated Solutions section.
2. Select the Flan-T5 XXL model under the Foundation Models category.
3. The Deployment Configuration section allows you to modify the hosting instance, endpoint name, and add additional tags. You can also change the S3 bucket location for storing the model artifact. For this demonstration, we will keep the settings at their default values.
4. Take note of the endpoint name for future use.
5. In the Security Settings section, specify the IAM role for creating the endpoint. You can also provide the VPC configurations, including subnets and security groups. Retrieve the subnet IDs and security group IDs from the VPC stack’s Outputs tab in the AWS CloudFormation console. Ensure at least two subnets are specified. These configurations control access to and from the model container.
6. Click “Deploy” to initiate the deployment process.
7. The status of the endpoint creation will be displayed in near-real-time, which may take several minutes to complete.
8. Take note of the “Model data location” field on the page. The SageMaker JumpStart models are hosted on a SageMaker managed S3 bucket (s3://jumpstart-cache-prod-{region}). Therefore, the model is deployed from this publicly accessible bucket and does not rely on public model zoo APIs.
9. If needed, the model artifact can be copied to a private model zoo or your own S3 bucket for enhanced control and security.
Testing the Deployed Flan-T5 XXL Model
To test the Flan-T5 XXL model, utilize the provided sample notebook available in SageMaker Studio. Follow these steps:
1. Open the notebook named “Use Endpoint from Studio.”
2. Choose “Data Science 3.0” as the kernel and “Python 3” as the kernel version.
3. Run the notebook cells to make predictions on the endpoint.
4. Note that the notebook leverages the invoke_endpoint() API or the SageMaker Python SDK’s predict() method to make predictions.
Advantages of Deploying SageMaker JumpStart Models in VPC Mode
There are several advantages to deploying SageMaker JumpStart models in VPC mode, including:
1. Enhanced security: Since SageMaker JumpStart does not download models from a public model zoo, it ensures increased security for the deployed models.
2. Improved performance: Deploying models within a VPC removes the need for models to rely on external network calls, enhancing performance and reducing latency.
3. Greater control: The ability to specify VPC configurations allows users to have granular control over model access, further bolstering security.
4. Scalability: Deploying models in a VPC enables easy scaling and facilitates seamless integration into existing infrastructures.
Conclusion
Amazon SageMaker JumpStart offers organizations the flexibility to deploy foundation models within their own secure Virtual Private Clouds (VPCs). By leveraging SageMaker JumpStart, businesses can harness the power of generative AI while ensuring compliance with regulatory requirements and maintaining network isolation. This article highlighted the steps to deploy a Flan-T5 XXL model in a VPC with no internet access using JumpStart. Additionally, it discussed the advantages of deploying SageMaker JumpStart models in VPC mode, including improved security, performance, control, and scalability.
Summary: Maximize Usage of VPC Mode with Zero Internet Connectivity using Amazon SageMaker JumpStart for Leveraging Generative AI Foundation Models
Generative AI, powered by large pre-trained models, is gaining traction in various industries for solving business challenges. However, organizations operating in regulated sectors need to run these AI solutions in their own protected environments, without internet connectivity. Amazon SageMaker JumpStart, an ML hub, offers a solution for deploying generative AI models in Virtual Private Clouds (VPCs). This article provides a step-by-step guide on how to set up a VPC with no internet connection, deploy the Flan T5-XXL generative AI model in the VPC using SageMaker JumpStart, and highlights the advantages of deploying JumpStart models in VPC mode, including enhanced security and reduced reliance on external resources.
Frequently Asked Questions:
Q1: What is machine learning and how does it work?
A1: Machine learning is a subset of artificial intelligence that enables machines to learn from data and improve their performance without being explicitly programmed. It works by utilizing algorithms and statistical models to analyze data, identify patterns, and make predictions or decisions.
Q2: What are the main types of machine learning?
A2: The main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the machine learns from labeled data to predict or classify new data. Unsupervised learning involves finding patterns or structures in unlabeled data. Reinforcement learning involves training a machine to make decisions based on trial and error and feedback from the environment.
Q3: What are the applications of machine learning?
A3: Machine learning has a wide range of applications across various industries. It is used in areas such as healthcare for medical diagnosis, finance for fraud detection, marketing for customer segmentation, autonomous vehicles for object recognition, and many more. The potential applications of machine learning are constantly expanding.
Q4: What are the challenges of implementing machine learning?
A4: Implementing machine learning can face challenges such as acquiring and preparing high-quality training data, selecting the appropriate algorithms and models, dealing with bias or ethical concerns in data, and ensuring scalability and efficiency in the learning process. Additionally, interpretability and explainability of machine learning models can also be a challenge.
Q5: How can businesses benefit from machine learning?
A5: Machine learning can provide numerous benefits to businesses. It can help automate repetitive tasks, improve decision-making processes, enhance customer experiences, optimize operations, and identify patterns or trends that can lead to business growth opportunities. By leveraging machine learning, businesses can gain a competitive edge and make data-driven decisions.
