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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://wiki.myamens.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](http://globalnursingcareers.com) ideas on AWS.<br>
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<br>In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the [designs](https://incomash.com) as well.<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](https://video-sharing.senhosts.com) that uses support discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating function is its support learning (RL) step, which was used to improve the design's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both [significance](https://squishmallowswiki.com) and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, implying it's geared up to break down intricate queries and factor through them in a detailed manner. This assisted reasoning process permits the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation model that can be integrated into numerous workflows such as agents, rational thinking and information analysis jobs.<br>
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, enabling effective reasoning by [routing queries](http://shammahglobalplacements.com) to the most relevant professional "clusters." This method allows the design to specialize in different problem domains while maintaining general 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 instance to deploy 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 thinking abilities of the main R1 model to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 design, [utilizing](https://gitlab.bzzndata.cn) it as an instructor [yewiki.org](https://www.yewiki.org/User:TitusOSullivan) design.<br>
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<br>You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://dev.clikviewstorage.com) design, we suggest deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock [Guardrails](https://localjobpost.com) to present safeguards, prevent damaging content, and assess designs against essential security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the [ApplyGuardrail API](https://gogs.greta.wywiwyg.net). You can create several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://careerportals.co.za) applications.<br>
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<br>Prerequisites<br>
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<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the [Service Quotas](http://git.jetplasma-oa.com) console and under AWS Services, select Amazon SageMaker, and confirm you're utilizing 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 deploying. To ask for a limit increase, develop a limit boost request and connect to your account team.<br>
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<br>Because you will be releasing this model with Guardrails, make certain you have the [proper AWS](https://partyandeventjobs.com) Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock [Guardrails](https://wishjobs.in). For instructions, see Establish permissions 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 harmful content, and assess models against key safety requirements. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and design responses released on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://harimuniform.co.kr). You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to [produce](http://travelandfood.ru) the guardrail, see the GitHub repo.<br>
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<br>The general flow involves the following steps: 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 out to the design for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is stepped in 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 show inference utilizing 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 foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:<br>
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<br>1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane.
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At the time of writing 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 provider and choose the DeepSeek-R1 model.<br>
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<br>The design detail page [supplies](https://hireteachers.net) important details about the design's capabilities, rates structure, and execution guidelines. You can find detailed usage directions, consisting of sample API calls and code bits for combination. The design supports various text generation tasks, including content creation, code generation, and concern answering, utilizing its support learning optimization and CoT reasoning abilities.
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The page likewise [consists](http://admin.youngsang-tech.com) of deployment options and licensing details to help you start with DeepSeek-R1 in your applications.
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3. To begin using DeepSeek-R1, pick Deploy.<br>
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<br>You will be [triggered](https://source.brutex.net) to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:AngusChamplin06) go into an endpoint name (between 1-50 alphanumeric characters).
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5. For Number of instances, go into a number of circumstances (in between 1-100).
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6. For example type, pick your circumstances type. For [yewiki.org](https://www.yewiki.org/User:BobBecker898) optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
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Optionally, you can configure advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role permissions, and file encryption settings. For most use cases, the default settings will work well. However, for production releases, you may desire to review these settings to align with your [company's security](http://git.emagenic.cl) and compliance requirements.
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7. Choose Deploy to start using the model.<br>
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<br>When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
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8. Choose Open in playground to access an interactive user 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 design template for optimum outcomes. For example, material for reasoning.<br>
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<br>This is an excellent way to check out the design's thinking and text generation abilities before integrating it into your applications. The play area supplies immediate feedback, helping you understand how the model responds to different inputs and letting you tweak your triggers for ideal outcomes.<br>
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<br>You can quickly [evaluate](https://jobs.ofblackpool.com) the design in the [play ground](https://78.47.96.1613000) through the UI. However, to invoke the deployed design programmatically with any [Amazon Bedrock](http://sdongha.com) APIs, you need to get the endpoint ARN.<br>
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<br>Run inference using guardrails with the released DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock [console](http://company-bf.com) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and sends a request to create text based on a user timely.<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, integrated algorithms, and prebuilt ML solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](http://43.136.17.1423000) to your usage case, with your data, and release them into production utilizing either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 design through [SageMaker JumpStart](https://reckoningz.com) offers two [hassle-free](https://git.polycompsol.com3000) approaches: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you pick the method that best fits 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, choose Studio in the navigation pane.
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2. First-time users will be triggered to develop a domain.
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3. On the SageMaker Studio console, [select JumpStart](https://ari-sound.aurumai.io) in the [navigation pane](http://47.120.70.168000).<br>
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<br>The design browser shows available designs, with details like the supplier name and model abilities.<br>
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
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Each model 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 appropriate), suggesting that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design<br>
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<br>5. Choose the model card to view the model [details](http://gitlab.solyeah.com) page.<br>
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<br>The [model details](https://gitea.egyweb.se) page includes the following details:<br>
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<br>- The design name and supplier details.
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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 specs.
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- Usage standards<br>
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<br>Before you release the design, it's [recommended](https://collegetalks.site) to evaluate the model details and license terms to verify compatibility with your use case.<br>
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<br>6. [Choose Deploy](http://193.9.44.91) to proceed with release.<br>
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<br>7. For Endpoint name, use the automatically generated name or develop a custom-made 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, enter the number of circumstances (default: 1).
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Selecting proper instance types and counts is crucial for expense and performance optimization. Monitor your deployment to change these settings 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 accuracy. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that [network seclusion](http://101.132.163.1963000) remains in location.
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11. Choose Deploy to release the model.<br>
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<br>The [deployment procedure](https://gitlab.tncet.com) can take a number of minutes to finish.<br>
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<br>When implementation is complete, your endpoint status will change to [InService](http://hmind.kr). At this moment, the model is all set to accept reasoning requests through the [endpoint](https://fumbitv.com). You can monitor the release development on the SageMaker console Endpoints page, which will display relevant 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.<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 essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for [deploying](http://194.87.97.823000) the design is provided in the Github here. You can clone the notebook and run 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](http://62.210.71.92) and run reasoning with your SageMaker JumpStart predictor<br>
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<br>Similar to Amazon Bedrock, you can also utilize 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 prevent unwanted charges, finish the steps in this area to clean 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 actions:<br>
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases.
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2. In the Managed implementations section, find the endpoint you wish to erase.
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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 right 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 deployed will [sustain costs](https://blogville.in.net) if you leave it running. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and [wiki.eqoarevival.com](https://wiki.eqoarevival.com/index.php/User:JaiMcginnis) 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 release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11953342) describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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://okoskalyha.hu) companies construct innovative options [utilizing](https://vazeefa.com) [AWS services](https://gogolive.biz) and sped up calculate. Currently, he is focused on developing strategies for fine-tuning and optimizing the reasoning performance of big language designs. In his spare time, Vivek takes pleasure in hiking, seeing motion pictures, and attempting different cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://demo.playtubescript.com) Specialist Solutions Architect with the Third-Party Model [Science](http://gitea.anomalistdesign.com) group at AWS. His location of focus is AWS [AI](https://wavedream.wiki) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
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<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://sossdate.com) 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://truthbook.social) center. She is enthusiastic about constructing services that help clients accelerate their [AI](http://98.27.190.224) journey and unlock company value.<br>
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