Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart
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DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md
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DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md
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<br>Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://ixoye.do)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://groupeudson.com) [concepts](http://193.140.63.43) on AWS.<br>
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<br>In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can steps to deploy the distilled variations of the designs too.<br>
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<br>Overview of DeepSeek-R1<br>
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<br>DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](http://worldwidefoodsupplyinc.com) that utilizes reinforcement learning to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its support knowing (RL) step, which was utilized to improve the [model's reactions](http://dev.nextreal.cn) beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's geared up to break down complex queries and reason through them in a detailed manner. This assisted reasoning process enables the design to produce more precise, transparent, and [detailed answers](https://git.alexhill.org). This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation model that can be incorporated into various workflows such as representatives, logical reasoning and information interpretation jobs.<br>
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, enabling effective inference by routing questions to the most relevant specialist "clusters." This approach allows the model to specialize in different problem domains while [maintaining](https://linked.aub.edu.lb) general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective models to [simulate](https://git.tasu.ventures) the behavior and [thinking patterns](http://clinicanevrozov.ru) of the larger DeepSeek-R1 model, using it as a teacher design.<br>
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<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and examine designs against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](http://www.book-os.com:3000) applications.<br>
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<br>Prerequisites<br>
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<br>To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the [Service Quotas](https://abileneguntrader.com) console and under AWS Services, select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To ask for a limit boost, develop a limitation boost demand and connect to your account group.<br>
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see Set up approvals to use guardrails for content filtering.<br>
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<br>Implementing guardrails with the ApplyGuardrail API<br>
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<br>Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging material, and evaluate models against key security criteria. You can carry out safety steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.<br>
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<br>The basic circulation involves the following steps: First, the system gets an input for the model. This input is then [processed](http://www.grandbridgenet.com82) through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a [message](http://ncdsource.kanghehealth.com) is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate reasoning using this API.<br>
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<br>Deploy DeepSeek-R1 in [Amazon Bedrock](https://www.dailynaukri.pk) Marketplace<br>
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<br>Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:<br>
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<br>1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane.
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At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.<br>
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<br>The design detail page supplies necessary details about the design's capabilities, rates structure, and application guidelines. You can discover detailed usage directions, including sample API calls and code bits for integration. The design supports different text generation jobs, [including](https://losangelesgalaxyfansclub.com) content development, code generation, and concern answering, utilizing its support discovering optimization and CoT thinking capabilities.
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The page also consists of [implementation options](https://hortpeople.com) and licensing details to assist you begin with DeepSeek-R1 in your applications.
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3. To start using DeepSeek-R1, choose Deploy.<br>
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<br>You will be triggered to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated.
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4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
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5. For Variety of instances, enter a [variety](https://umindconsulting.com) of instances (between 1-100).
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6. For example type, [raovatonline.org](https://raovatonline.org/author/gailziegler/) select your instance type. For ideal [efficiency](https://europlus.us) with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
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Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you might wish to review these settings to align with your organization's security and compliance requirements.
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7. Choose Deploy to begin using the model.<br>
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<br>When the implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
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8. Choose Open in play area to access an interactive interface where you can try out various prompts and change design specifications like temperature level and optimum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, material for reasoning.<br>
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<br>This is an excellent method to explore the [model's reasoning](http://47.120.16.1378889) and [text generation](https://git.nothamor.com3000) abilities before integrating it into your [applications](https://git.adminkin.pro). The play area [supplies instant](http://101.36.160.14021044) feedback, assisting you comprehend how the model reacts to various inputs and letting you tweak your prompts for ideal results.<br>
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<br>You can quickly evaluate the model in the play ground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
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<br>Run inference using guardrails with the deployed DeepSeek-R1 endpoint<br>
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<br>The following code example demonstrates how to perform inference using a [released](http://harimuniform.co.kr) DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the [GitHub repo](https://git.andy.lgbt). After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends a demand to generate text based on a user prompt.<br>
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<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
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<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into production utilizing either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the technique that finest suits your needs.<br>
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
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<br>Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:<br>
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<br>1. On the SageMaker console, select Studio in the navigation pane.
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2. First-time users will be triggered to create a domain.
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3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
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<br>The design web browser displays available designs, with details like the service provider name and [model capabilities](https://tageeapp.com).<br>
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
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Each model card shows key details, consisting of:<br>
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<br>- Model name
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- Provider name
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- Task category (for instance, Text Generation).
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Bedrock Ready badge (if appropriate), suggesting that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design<br>
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<br>5. Choose the design card to view the model details page.<br>
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<br>The design details page consists of the following details:<br>
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<br>- The design name and supplier details.
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[Deploy button](http://47.109.153.573000) to deploy the model.
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About and Notebooks tabs with detailed details<br>
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<br>The About tab includes essential details, [wavedream.wiki](https://wavedream.wiki/index.php/User:ClaireSparling1) such as:<br>
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<br>- Model description.
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- License details.
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- Technical specs.
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- Usage guidelines<br>
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<br>Before you release the design, it's suggested to review the model details and license terms to [verify compatibility](https://sodam.shop) with your usage case.<br>
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<br>6. Choose Deploy to proceed with release.<br>
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<br>7. For Endpoint name, utilize the automatically produced name or develop a customized one.
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8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial instance count, enter the number of circumstances (default: 1).
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Selecting appropriate [circumstances types](http://gitlab.awcls.com) and counts is important for cost and performance optimization. Monitor your deployment to change these [settings](https://abalone-emploi.ch) as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
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10. Review all setups for precision. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
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11. Choose Deploy to deploy the model.<br>
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<br>The deployment process can take several minutes to finish.<br>
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<br>When release is complete, your endpoint status will change to [InService](http://119.167.221.1460000). At this moment, the design is ready to accept reasoning demands through the endpoint. You can monitor the [implementation progress](https://classificados.diariodovale.com.br) on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can [conjure](https://deadreckoninggame.com) up the model utilizing a SageMaker runtime client and integrate it with your applications.<br>
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<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
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<br>To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the [SageMaker Python](http://103.197.204.1623025) SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
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<br>You can run extra demands against the predictor:<br>
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<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
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<br>Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br>
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<br>Tidy up<br>
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<br>To avoid unwanted charges, finish the actions in this area to tidy up your resources.<br>
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<br>Delete the [Amazon Bedrock](https://accc.rcec.sinica.edu.tw) Marketplace implementation<br>
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<br>If you released the model using Amazon Bedrock Marketplace, complete the following actions:<br>
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations.
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2. In the Managed releases section, find the endpoint you wish to delete.
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3. Select the endpoint, and on the Actions menu, select Delete.
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4. Verify the endpoint details to make certain you're erasing the correct release: 1. [Endpoint](https://www.ayuujk.com) name.
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2. Model name.
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3. Endpoint status<br>
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<br>Delete the SageMaker JumpStart predictor<br>
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<br>The SageMaker JumpStart model you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and [Resources](https://bethanycareer.com).<br>
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<br>Conclusion<br>
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<br>In this post, we [explored](https://youtubegratis.com) how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with Amazon SageMaker JumpStart.<br>
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<br>About the Authors<br>
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<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://customerscomm.com) business develop innovative services utilizing AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the inference efficiency of big language designs. In his downtime, Vivek enjoys treking, enjoying films, and attempting various cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.trov.ar) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.nullstate.net) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
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<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](http://8.141.83.223:3000) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://jatushome.myqnapcloud.com:8090) center. She is passionate about building solutions that assist clients accelerate their [AI](https://aggeliesellada.gr) journey and unlock organization value.<br>
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