Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are thrilled 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](http://git.sdkj001.cn)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion [criteria](http://111.160.87.828004) to construct, experiment, and responsibly scale your generative [AI](https://git.chartsoft.cn) 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 steps to deploy the distilled versions of the models as well.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://spudz.org) that uses reinforcement discovering to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating feature is its reinforcement learning (RL) step, which was utilized to fine-tune the design's actions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 utilizes a [chain-of-thought](http://git.jihengcc.cn) (CoT) approach, [implying](https://gogs.fytlun.com) it's geared up to break down complicated queries and factor through them in a detailed way. This assisted reasoning process enables the model to produce more precise, transparent, and detailed answers. This design combines [RL-based fine-tuning](https://117.50.190.293000) with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the market's attention as a versatile text-generation model that can be integrated into different workflows such as agents, rational thinking and data analysis tasks.<br> |
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, making it possible for efficient reasoning by routing questions to the most pertinent specialist "clusters." This technique permits the model to concentrate on various problem domains while maintaining general efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will [utilize](https://medicalstaffinghub.com) an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient models to [imitate](https://rocksoff.org) the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor 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 design, we advise deploying this model with guardrails in [location](https://bytes-the-dust.com). In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and evaluate designs against key safety requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://www.jobindustrie.ma) applications.<br> |
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<br>Prerequisites<br> |
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<br>To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limitation increase, develop a limitation boost demand and reach out to your account group.<br> |
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<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the [correct AWS](https://sun-clinic.co.il) Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use 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 present safeguards, avoid hazardous material, and evaluate designs against essential security requirements. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, [gratisafhalen.be](https://gratisafhalen.be/author/danarawson/) see the GitHub repo.<br> |
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<br>The general circulation includes the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](http://135.181.29.1743001) check, it's sent to the model for inference. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's [returned](https://westzoneimmigrations.com) 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 in the following areas show [reasoning](https://social.ishare.la) 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 models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:<br> |
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<br>1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the [navigation pane](https://speeddating.co.il). |
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At the time of [composing](https://wellandfitnessgn.co.kr) this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.<br> |
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<br>The design detail page offers essential details about the model's capabilities, prices structure, and execution guidelines. You can find detailed use guidelines, consisting of sample API calls and code snippets for combination. The model supports numerous text generation jobs, including material development, code generation, and question answering, utilizing its support discovering optimization and CoT thinking capabilities. |
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The page also includes implementation choices and licensing details to assist you begin with DeepSeek-R1 in your applications. |
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3. To start using DeepSeek-R1, pick Deploy.<br> |
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<br>You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. |
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4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Number of instances, enter a variety of instances (in between 1-100). |
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6. For Instance type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a [GPU-based](http://www.xn--2i4bi0gw9ai2d65w.com) instance type like ml.p5e.48 xlarge is recommended. |
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Optionally, you can configure sophisticated [security](http://oj.algorithmnote.cn3000) and infrastructure settings, including virtual private cloud (VPC) networking, service role permissions, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you may desire to evaluate these settings to line up with your company'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 test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. |
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8. Choose Open in playground to access an interactive interface where you can try out different prompts and change design criteria like temperature level and optimum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, content for reasoning.<br> |
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<br>This is an exceptional method to explore the model's reasoning and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:FidelBatt531106) text generation abilities before incorporating it into your applications. The playground supplies instant feedback, assisting you understand how the design reacts to numerous inputs and letting you tweak your prompts for optimal outcomes.<br> |
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<br>You can [rapidly](http://ufidahz.com.cn9015) test the model in the play ground 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 reasoning using guardrails with the deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to carry out reasoning using a [released](https://git2.nas.zggsong.cn5001) DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, [it-viking.ch](http://it-viking.ch/index.php/User:Dianna01H6) sets up reasoning specifications, and sends out a request to create [text based](http://61.174.243.2815863) 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](https://zomi.watch) (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](http://hualiyun.cc3568) to your use case, with your data, and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:KarissaGleason) deploy them into production utilizing either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical approaches: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you pick the approach that finest suits your [requirements](https://www.elitistpro.com).<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 utilizing 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 prompted to create a domain. |
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3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br> |
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<br>The design browser shows available designs, with details like the [company](https://starleta.xyz) name and model capabilities.<br> |
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. |
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Each model card reveals essential details, including:<br> |
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<br>- Model name |
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- Provider name |
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- Task category (for instance, Text Generation). |
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Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design<br> |
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<br>5. Choose the model 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. |
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Deploy button to deploy the design. |
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About and Notebooks tabs with [detailed](http://git.jihengcc.cn) details<br> |
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<br>The About tab includes crucial 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 to review the design details and license terms to confirm compatibility with your use case.<br> |
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<br>6. Choose Deploy to continue with [release](https://www.gc-forever.com).<br> |
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<br>7. For Endpoint name, use the instantly created name or develop a custom 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, go into the number of [instances](http://103.140.54.203000) (default: 1). |
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[Selecting](http://154.64.253.773000) appropriate instance types and counts is important for expense and performance optimization. Monitor your deployment to change these settings as needed.Under [Inference](https://jobs.careersingulf.com) type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. |
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10. Review all configurations for accuracy. For this model, we strongly suggest sticking to [SageMaker JumpStart](https://iklanbaris.id) default settings and making certain that network isolation remains in location. |
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11. Choose Deploy to release the model.<br> |
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<br>The release process 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. At this moment, the model is all set to accept inference demands through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will [display pertinent](http://flexchar.com) metrics and status details. When the implementation is total, you can [conjure](https://champ217.flixsterz.com) up the design utilizing a SageMaker runtime client and incorporate it with your applications.<br> |
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<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br> |
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<br>To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Arturo0965) you will need to set up the [SageMaker Python](http://112.74.102.696688) SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for [releasing](http://gitlab.boeart.cn) the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.<br> |
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<br>You can run extra demands against the predictor:<br> |
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<br>Implement guardrails and run [inference](https://ready4hr.com) with your SageMaker JumpStart 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 utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:<br> |
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<br>Clean up<br> |
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<br>To avoid undesirable charges, finish the actions in this section to tidy up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace implementation<br> |
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<br>If you [released](https://1.214.207.4410333) the design utilizing Amazon Bedrock Marketplace, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. |
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2. In the Managed deployments section, locate the [endpoint](http://47.119.175.53000) you wish to erase. |
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3. Select the endpoint, and on the Actions menu, . |
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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 model you released will sustain costs 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 explored how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker [JumpStart](https://social.mirrororg.com). Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with Amazon SageMaker JumpStart.<br> |
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<br>About the Authors<br> |
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<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://impactosocial.unicef.es) companies construct ingenious options utilizing AWS services and accelerated compute. Currently, he is focused on establishing strategies for fine-tuning and optimizing the reasoning performance of big language models. In his spare time, Vivek delights in treking, [viewing](https://wamc1950.com) films, and trying various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://zamhi.net) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://43.139.10.64:3000) [accelerators](https://sublimejobs.co.za) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://www.jacksonhampton.com:3000) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for [Amazon SageMaker](https://www.jobindustrie.ma) JumpStart, SageMaker's artificial intelligence and generative [AI](https://thematragroup.in) center. She is passionate about building solutions that help consumers accelerate their [AI](https://wiki.project1999.com) journey and unlock organization worth.<br> |
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