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 thrilled to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://nextjobnepal.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://try.gogs.io) ideas on AWS.<br>
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<br>In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions 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 big language design (LLM) established by DeepSeek [AI](http://drive.ru-drive.com) that utilizes reinforcement finding out to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying feature is its support knowing (RL) action, which was utilized to improve the design's actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt more successfully to user feedback and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:CarmelPerdue) objectives, ultimately improving both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, [meaning](http://www.my.vw.ru) it's geared up to break down complicated inquiries and factor through them in a detailed way. This guided reasoning procedure permits the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user [interaction](http://101.200.241.63000). With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be integrated into various workflows such as agents, rational thinking and data interpretation jobs.<br>
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, allowing efficient inference by routing inquiries to the most appropriate expert "clusters." This method permits the design to specialize in various issue domains while maintaining overall efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to [release](https://govtpakjobz.com) the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective models to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher model.<br>
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<br>You can [release](https://puzzle.thedimeland.com) DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, [yewiki.org](https://www.yewiki.org/User:LeonelBonnor563) we recommend deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to [introduce](http://www.hyakuyichi.com3000) safeguards, avoid hazardous content, and evaluate models against crucial security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://www.telix.pl) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To request a limit increase, develop a limit boost request and reach out to your account team.<br>
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<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see [Establish](https://www.jr-it-services.de3000) approvals to utilize 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 present safeguards, prevent damaging content, and evaluate models against [essential safety](https://rabota-57.ru) requirements. You can implement security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.<br>
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<br>The basic flow involves the following steps: First, the system receives an input for the design. This input is then processed 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 [applied](https://dvine.tv). If the output passes this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is [returned](https://www.wakewiki.de) showing the nature of the intervention and whether it took place 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 Marketplace<br>
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<br>Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1009888) specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br>
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<br>1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane.
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At the time of composing this post, you can use 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](http://internetjo.iwinv.net) as a service provider and choose the DeepSeek-R1 design.<br>
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<br>The design detail page supplies necessary details about the model's abilities, rates structure, and execution guidelines. You can find detailed usage directions, consisting of sample API calls and code snippets for integration. The design supports various text generation tasks, including material creation, code generation, and concern answering, utilizing its support discovering optimization and CoT thinking capabilities.
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The page also [consists](https://novashop6.com) of implementation alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications.
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3. To start utilizing DeepSeek-R1, select Deploy.<br>
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<br>You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
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5. For Variety of instances, go into a number of instances (between 1-100).
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6. For example type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
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Optionally, you can set up innovative security and infrastructure settings, consisting of [virtual personal](https://copyrightcontest.com) cloud (VPC) networking, service function authorizations, and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GemmaJenson1) encryption settings. For most use cases, the default settings will work well. However, for [production](https://git.ivabus.dev) deployments, you may wish to examine these settings to align with your organization's security and compliance requirements.
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7. Choose Deploy to begin using the design.<br>
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<br>When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
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8. Choose Open in play ground to access an interactive interface where you can experiment with different prompts and change model parameters like temperature level and optimum length.
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, content for inference.<br>
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<br>This is an outstanding method to explore the model's reasoning and text generation abilities before [integrating](http://gitlab.zbqdy666.com) it into your applications. The playground offers instant feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your triggers for ideal outcomes.<br>
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<br>You can rapidly test the model in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
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<br>Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the [Amazon Bedrock](https://git.andrewnw.xyz) [console](https://medicalstaffinghub.com) or the API. For the example code to develop the guardrail, see the [GitHub repo](https://gitea.alaindee.net). After you have developed the guardrail, utilize the following code to [implement guardrails](https://www.cvgods.com). The script initializes the bedrock_runtime customer, sets up inference parameters, and sends out a demand to create 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) center with FMs, integrated algorithms, and prebuilt ML [options](http://182.92.251.553000) that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 convenient methods: utilizing the intuitive SageMaker JumpStart UI or carrying out 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 actions 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 prompted to develop 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 model web browser displays available models, with details like the supplier name and [design capabilities](http://117.50.220.1918418).<br>
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
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Each model card reveals key details, consisting of:<br>
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<br>- Model name
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- Provider name
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- Task classification (for example, Text Generation).
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Bedrock Ready badge (if appropriate), [suggesting](http://www.xn--80agdtqbchdq6j.xn--p1ai) that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up 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 model details page includes the following details:<br>
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<br>- The design name and [company details](https://git.sommerschein.de).
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Deploy button to deploy the model.
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About and Notebooks tabs with detailed details<br>
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<br>The About tab consists of important details, such as:<br>
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<br>- Model description.
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- License details.
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- Technical requirements.
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- Usage guidelines<br>
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<br>Before you release the design, it's recommended to examine the model details and license terms to validate compatibility with your usage case.<br>
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<br>6. Choose Deploy to continue with implementation.<br>
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<br>7. For Endpoint name, use the automatically produced name or develop a customized one.
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8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial instance count, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11910346) get in the number of instances (default: 1).
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Selecting suitable instance types and counts is crucial for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under [Inference](http://106.14.65.137) type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
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10. Review all setups for accuracy. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
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11. Choose Deploy to deploy the design.<br>
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<br>The deployment procedure can take [numerous](http://plus.ngo) minutes to finish.<br>
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<br>When implementation is total, your endpoint status will change to InService. At this moment, the model is prepared to accept reasoning demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can invoke the design using a SageMaker runtime client and integrate it with your [applications](https://git.kitgxrl.gay).<br>
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<br>Deploy DeepSeek-R1 [utilizing](https://www.hirerightskills.com) the SageMaker Python SDK<br>
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<br>To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for [reasoning programmatically](http://enhr.com.tr). The code for releasing the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.<br>
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<br>You can run extra requests against the predictor:<br>
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<br> guardrails and run reasoning with your SageMaker JumpStart predictor<br>
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<br>Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and [execute](https://git.mikecoles.us) it as displayed in the following code:<br>
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<br>Tidy up<br>
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<br>To prevent unwanted charges, complete the actions in this area to clean up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace deployment<br>
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<br>If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:<br>
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
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2. In the Managed implementations area, locate the endpoint you wish to delete.
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3. Select the endpoint, and on the Actions menu, choose Delete.
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4. Verify the endpoint details to make certain you're erasing the correct implementation: 1. Endpoint 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 released will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
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<br>Conclusion<br>
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<br>In this post, we explored how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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 helps emerging generative [AI](https://knightcomputers.biz) business build innovative services using AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the reasoning performance of large language designs. In his spare time, Vivek delights in treking, seeing movies, and attempting various foods.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://videoflixr.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://101.42.90.121:3000) 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](https://candays.com) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://jobs.competelikepros.com) center. She is enthusiastic about building options that assist customers accelerate their [AI](https://twwrando.com) journey and unlock business worth.<br>
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