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 release DeepSeek [AI](https://axionrecruiting.com)'s [first-generation frontier](http://kyeongsan.co.kr) model, DeepSeek-R1, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) together with the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://www.cowgirlboss.com) concepts on AWS.<br>
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<br>In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the models 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) established by DeepSeek [AI](http://kodkod.kr) that utilizes support discovering to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying function is its support knowing (RL) action, which was used to refine the design's reactions beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down complex queries and factor through them in a detailed manner. This assisted reasoning procedure permits the model to produce more accurate, transparent, and detailed answers. This design [combines RL-based](https://careerconnect.mmu.edu.my) fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually [captured](https://audioedu.kyaikkhami.com) the industry's attention as a flexible [text-generation model](https://gogs.greta.wywiwyg.net) that can be incorporated into numerous workflows such as agents, sensible reasoning and data interpretation jobs.<br>
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, enabling efficient inference by routing inquiries to the most appropriate professional "clusters." This approach permits the design to focus on various issue domains while maintaining overall effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective designs to mimic the behavior and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor model.<br>
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<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock Marketplace](https://nepaxxtube.com). Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and evaluate models against key safety criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails [tailored](http://106.14.174.2413000) to different usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security [controls](http://175.27.215.923000) throughout your generative [AI](https://35.237.164.2) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas [console](http://1.14.125.63000) and under AWS Services, choose Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limit boost, produce a limit increase demand and reach out 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 proper AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Set up authorizations to utilize guardrails for material filtering.<br>
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<br>Implementing guardrails with the ApplyGuardrail API<br>
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<br>Amazon Bedrock Guardrails enables you to introduce safeguards, avoid hazardous material, and examine designs against essential safety criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design actions 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 produce the guardrail, see the [GitHub repo](https://wiki.contextgarden.net).<br>
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<br>The general flow includes the following actions: First, the system [receives](https://earlyyearsjob.com) an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](https://social.oneworldonesai.com) check, it's sent out to the design for reasoning. After receiving the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the last result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections demonstrate [reasoning](https://swaggspot.com) 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 specialized structure designs (FMs) through [Amazon Bedrock](https://upi.ind.in). 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, choose Model [brochure](http://epsontario.com) 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 design. It does not support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.<br>
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<br>The model detail page offers important details about the model's abilities, rates structure, and execution guidelines. You can discover detailed use instructions, including [sample API](https://gogs.macrotellect.com) calls and code bits for integration. The model supports various text generation jobs, including content production, code generation, and concern answering, utilizing its support finding out optimization and CoT thinking abilities.
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The page likewise consists of and licensing details to assist you begin with DeepSeek-R1 in your applications.
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3. To begin utilizing DeepSeek-R1, pick Deploy.<br>
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<br>You will be prompted to configure 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 circumstances (in between 1-100).
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6. For example type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
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Optionally, you can set up innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you might desire to examine these settings to line up with your company'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 complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
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8. Choose Open in play area to access an interactive interface where you can explore different prompts and change design criteria like temperature and maximum length.
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, material for reasoning.<br>
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<br>This is an exceptional way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, helping you understand how the model reacts to numerous inputs and letting you tweak your prompts for optimum outcomes.<br>
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<br>You can rapidly check the design in the [playground](https://wheeoo.com) through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
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<br>Run inference using [guardrails](http://www.jobteck.co.in) with the released DeepSeek-R1 endpoint<br>
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<br>The following code example demonstrates how to carry out inference utilizing a deployed 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 develop the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends out a demand to generate text based upon a user timely.<br>
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<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
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<br>[SageMaker JumpStart](https://bpx.world) is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can release with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://aidesadomicile.ca) models to your usage case, with your information, and deploy them into production using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart offers two practical methods: [utilizing](http://209.141.61.263000) the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you select the approach that finest matches your requirements.<br>
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
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<br>Complete the following steps to deploy 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](https://whotube.great-site.net) users will be prompted to produce a domain.
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3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br>
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<br>The model web browser shows available designs, with details like the supplier name and model capabilities.<br>
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
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Each design card shows crucial details, consisting of:<br>
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<br>- Model name
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- Provider name
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- Task classification (for instance, Text Generation).
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Bedrock Ready badge (if applicable), suggesting that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design<br>
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<br>5. Choose the model card to view the design details page.<br>
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<br>The design details page includes the following details:<br>
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<br>- The design name and provider details.
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Deploy button to release the model.
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About and Notebooks tabs with detailed details<br>
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<br>The About [tab consists](https://esvoe.video) of important details, such as:<br>
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<br>- Model description.
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- License details.
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- Technical specifications.
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- Usage guidelines<br>
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<br>Before you release the model, it's recommended to evaluate 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 deployment.<br>
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<br>7. For [Endpoint](https://ai.ceo) name, utilize the automatically generated name or create 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 circumstances count, go into the variety of instances (default: 1).
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Selecting suitable instance types and counts is vital for expense and efficiency optimization. Monitor your [release](https://cello.cnu.ac.kr) to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency.
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10. Review all setups for accuracy. For this model, we highly advise sticking to [SageMaker JumpStart](http://116.204.119.1713000) default settings and making certain that network seclusion remains in location.
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11. Choose Deploy to deploy the model.<br>
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<br>The release procedure can take several minutes to finish.<br>
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<br>When implementation is complete, your endpoint status will change to InService. At this moment, the model is prepared to [accept inference](https://finance.azberg.ru) [requests](http://www.asystechnik.com) through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is total, you can invoke the design utilizing a SageMaker runtime customer and incorporate it with your applications.<br>
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<br>Deploy DeepSeek-R1 [utilizing](https://ramique.kr) the SageMaker Python SDK<br>
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<br>To start with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the [notebook](http://git.z-lucky.com90) and range from SageMaker Studio.<br>
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<br>You can run additional requests against the predictor:<br>
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<br>Implement guardrails and run reasoning with your [SageMaker JumpStart](https://www.jobplanner.eu) predictor<br>
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<br>Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as [displayed](https://voggisper.com) in the following code:<br>
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<br>Tidy up<br>
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<br>To prevent unwanted charges, finish the steps in this area to tidy up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace release<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 [implementations](https://eurosynapses.giannistriantafyllou.gr).
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2. In the Managed implementations area, locate the endpoint you desire 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 deleting the correct release: 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 design you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you wish 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 checked out how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:CarmelPerdue) Inference at AWS. He assists emerging generative [AI](https://git.cloud.krotovic.com) business develop ingenious solutions using AWS services and accelerated calculate. Currently, he is focused on [developing strategies](https://elitevacancies.co.za) for fine-tuning and optimizing the reasoning efficiency of big language designs. In his [leisure](http://47.95.167.2493000) time, Vivek takes pleasure in treking, seeing movies, and attempting different cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://www.jr-it-services.de:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://123.56.247.193:3000) [accelerators](http://121.40.194.1233000) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
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<br>[Jonathan Evans](http://hrplus.com.vn) is an Expert Solutions Architect working on generative [AI](http://47.95.167.249:3000) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads item, engineering, and tactical collaborations for [Amazon SageMaker](https://git.thewebally.com) JumpStart, SageMaker's [artificial intelligence](https://redefineworksllc.com) and generative [AI](http://www.xn--80agdtqbchdq6j.xn--p1ai) center. She is passionate about constructing solutions that assist clients accelerate their [AI](https://community.scriptstribe.com) journey and unlock service value.<br>
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