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<br>Today, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:TracieCoats00) we are excited to reveal 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://cacklehub.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your [generative](https://git.yqfqzmy.monster) [AI](https://tradingram.in) concepts 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 follow comparable actions to [release](http://orcz.com) 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) developed by DeepSeek [AI](https://psuconnect.in) that uses reinforcement learning to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential identifying function is its support knowing (RL) action, which was [utilized](http://101.42.41.2543000) to refine the design's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately enhancing both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's equipped to break down complicated inquiries and factor through them in a detailed way. This guided thinking procedure enables the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, logical thinking and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Rosalind2029) data analysis tasks.<br>
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, making it possible for efficient inference by [routing queries](http://218.201.25.1043000) to the most appropriate expert "clusters." This method allows the model to specialize in various issue domains while [maintaining](http://94.191.100.41) general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 [xlarge circumstances](https://pakalljobs.live) to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher model.<br>
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<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock Marketplace](https://prantle.com). Because DeepSeek-R1 is an emerging design, we suggest [releasing](https://kiwiboom.com) this model with guardrails in [location](http://103.235.16.813000). In this blog site, we will utilize Amazon Bedrock Guardrails to [introduce](https://www.cartoonistnetwork.com) safeguards, avoid damaging material, and assess models against key safety criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](http://140.143.226.1) applications.<br>
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<br>Prerequisites<br>
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<br>To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 circumstances in the AWS Region you are deploying. To ask for a limitation increase, [develop](http://101.43.112.1073000) a limit boost request and connect to your account group.<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) permissions to use Amazon Bedrock Guardrails. For guidelines, see Establish consents 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, prevent hazardous content, and [it-viking.ch](http://it-viking.ch/index.php/User:MonikaTempleton) examine models against essential security criteria. You can [implement precaution](http://forum.moto-fan.pl) for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock [console](http://39.105.128.46) or the API. For the example code to create the guardrail, see the GitHub repo.<br>
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<br>The basic flow involves the following actions: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for inference. After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. The examples [showcased](https://barokafunerals.co.za) in the following areas show [reasoning](https://git.sortug.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 provides you access to over 100 popular, emerging, and specialized foundation 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, choose Model brochure under [Foundation](http://git.sinoecare.com) designs in the navigation pane.
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At the time of writing this post, you can use the InvokeModel API to invoke the model. It does not 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 model detail page provides important details about the model's abilities, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:WBKJosef646) rates structure, and implementation standards. You can find detailed usage directions, consisting of sample API calls and code snippets for combination. The model supports various text generation tasks, consisting of content production, code generation, and concern answering, [utilizing](https://git.gday.express) its reinforcement discovering [optimization](https://dimans.mx) and CoT reasoning capabilities.
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The page likewise consists of implementation choices and licensing details to help you get going with DeepSeek-R1 in your applications.
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3. To begin [utilizing](https://git.yingcaibx.com) DeepSeek-R1, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:RichieFirkins) select Deploy.<br>
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<br>You will be triggered to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, enter an endpoint name (between 1-50 [alphanumeric](http://150.158.93.1453000) characters).
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5. For Number of circumstances, enter a variety of circumstances (between 1-100).
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6. For example type, choose your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
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Optionally, you can set up sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, 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 utilizing the model.<br>
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<br>When the implementation is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
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8. Choose Open in play ground to access an interactive interface where you can try out different prompts and change model 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 optimal outcomes. For example, material for inference.<br>
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<br>This is an exceptional method to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The play ground supplies instant feedback, helping you understand how the design reacts to numerous inputs and letting you tweak your prompts for optimum outcomes.<br>
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<br>You can rapidly test the model in the play area through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
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<br>Run inference using guardrails with the [released](http://1.92.66.293000) DeepSeek-R1 endpoint<br>
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<br>The following code example [demonstrates](https://menfucks.com) how to perform inference utilizing a deployed DeepSeek-R1 design through [Amazon Bedrock](http://experienciacortazar.com.ar) 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. After you have actually developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures 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) center with FMs, integrated algorithms, and [raovatonline.org](https://raovatonline.org/author/namchism044/) prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, 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 two convenient approaches: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the technique that finest matches 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 deploy DeepSeek-R1 utilizing 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, select JumpStart in the navigation pane.<br>
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<br>The model internet browser displays available designs, with details like the service provider name and model capabilities.<br>
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
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Each [design card](http://isarch.co.kr) [reveals](http://43.136.17.1423000) 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 suitable), suggesting that this design can be registered with Amazon Bedrock, allowing you to use [Amazon Bedrock](https://mixup.wiki) APIs to invoke the model<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](https://git.aiadmin.cc) page includes the following details:<br>
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<br>- The model name and supplier 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 includes essential 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 deploy the model, it's suggested to evaluate the design details and license terms to confirm compatibility with your usage case.<br>
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<br>6. Choose Deploy to proceed with implementation.<br>
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<br>7. For [Endpoint](https://wiki.lafabriquedelalogistique.fr) name, utilize the instantly produced name or produce a [custom-made](https://platform.giftedsoulsent.com) one.
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8. For example [type ¸](https://dyipniflix.com) pick an instance type (default: ml.p5e.48 xlarge).
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9. For Initial circumstances count, enter the number of instances (default: 1).
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[Selecting proper](https://beta.talentfusion.vn) [circumstances](https://clearcreek.a2hosted.com) types and counts is essential for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
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10. Review all configurations for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
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11. Choose Deploy to release the design.<br>
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<br>The release procedure can take several minutes to finish.<br>
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<br>When deployment is complete, your endpoint status will alter to InService. At this point, the design is prepared to accept reasoning requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can conjure up the design utilizing a SageMaker runtime customer and integrate it with your applications.<br>
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<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
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<br>To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model 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 additional requests against the predictor:<br>
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<br>[Implement guardrails](https://complexityzoo.net) and run inference 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 produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:<br>
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<br>Tidy up<br>
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<br>To avoid [unwanted](http://47.92.27.1153000) charges, finish the steps in this section 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 released the model using Amazon Bedrock Marketplace, total the following actions:<br>
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<br>1. On the Amazon Bedrock console, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:EdithJoseph92) under Foundation designs in the navigation pane, pick Marketplace deployments.
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2. In the Managed deployments area, find the endpoint you want to erase.
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3. Select the endpoint, and on the Actions menu, pick Delete.
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4. Verify the endpoint details to make certain you're deleting the [correct](https://gitea.alaindee.net) deployment: 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 deployed will sustain expenses 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 release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon [Bedrock Marketplace](http://lyo.kr) now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with [Amazon SageMaker](http://47.103.112.133) 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://git.riomhaire.com) [business build](https://pittsburghpenguinsclub.com) ingenious services using AWS services and accelerated compute. Currently, he is concentrated on developing methods for fine-tuning and optimizing the reasoning performance of large language designs. In his spare time, Vivek takes pleasure in hiking, watching films, and trying different cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://www.elcel.org) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://fromkorea.kr) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
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<br>Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://techport.io) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://dev.ncot.uk) hub. She is enthusiastic about [constructing services](http://orcz.com) that assist customers accelerate their [AI](https://okk-shop.com) journey and unlock organization value.<br>
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