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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<br>Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](http://39.106.177.1608756) JumpStart. With this launch, you can now release DeepSeek [AI](https://nodlik.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](http://154.9.255.198:3000) ideas 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](http://41.111.206.1753000). You can follow similar steps to release 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) established by DeepSeek [AI](https://jskenglish.com) that uses reinforcement learning to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating feature is its reinforcement learning (RL) action, which was [utilized](https://tv.lemonsocial.com) to refine the design's reactions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex questions and factor through them in a detailed way. This guided thinking procedure allows the model to [produce](https://git.molokoin.ru) more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be integrated into various workflows such as representatives, sensible thinking and data analysis tasks.<br>
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, enabling effective reasoning by routing questions to the most relevant professional "clusters." This approach allows the model to concentrate on various problem domains while maintaining total performance. 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based on popular open [designs](https://www.athleticzoneforum.com) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more [efficient models](https://xn--9m1bq6p66gu3avit39e.com) to mimic the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher model.<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 releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and [assess designs](https://git.xaviermaso.com) against key security requirements. At the time of writing this blog site, for DeepSeek-R1 [deployments](https://git.xjtustei.nteren.net) on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, enhancing user [experiences](https://superblock.kr) and standardizing safety controls across your generative [AI](https://www.jobindustrie.ma) 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 instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge [circumstances](http://gitea.infomagus.hu) in the AWS Region you are releasing. To request a limitation increase, produce a limitation boost demand 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) approvals to use Amazon Bedrock [Guardrails](https://cruyffinstitutecareers.com). For directions, see Establish permissions to utilize guardrails for content filtering.<br>
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<br>Implementing guardrails with the ApplyGuardrail API<br>
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<br>Amazon Bedrock Guardrails permits you to [introduce](http://git.sinoecare.com) safeguards, prevent hazardous material, and examine models against key security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model 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.<br>
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<br>The general flow involves 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 check, it's sent to the model for reasoning. After receiving the model's output, another [guardrail check](http://119.3.9.593000) is used. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the [intervention](https://taar.me) and whether it occurred at the input or output stage. The examples showcased in the following areas 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 provides you access to over 100 popular, emerging, and specialized structure designs (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, pick Model brochure under [Foundation models](https://callingirls.com) in the navigation pane.
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At the time of composing this post, you can utilize 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](http://gitlab.suntrayoa.com) and select the DeepSeek-R1 design.<br>
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<br>The design detail page offers essential details about the design's abilities, prices structure, and application standards. You can find detailed usage guidelines, including sample API calls and code bits for combination. The [model supports](https://yeetube.com) various text generation tasks, including content creation, code generation, and concern answering, [utilizing](https://dash.bss.nz) its reinforcement finding out optimization and CoT thinking abilities.
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The page also consists of implementation options and licensing details to help you get going with DeepSeek-R1 in your applications.
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3. To begin using DeepSeek-R1, select Deploy.<br>
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<br>You will be prompted to configure the release 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 variety of circumstances (between 1-100).
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6. For Instance type, choose your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
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Optionally, you can configure sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function consents, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may desire to examine these [settings](https://code.thintz.com) to align with your organization's security and compliance requirements.
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7. Choose Deploy to begin using the model.<br>
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<br>When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
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8. Choose Open in playground to access an interactive user interface where you can try out different prompts and adjust model criteria like temperature and optimum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, material for reasoning.<br>
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<br>This is an outstanding way to check out the model's thinking and text generation capabilities before incorporating it into your applications. The play ground offers instant feedback, helping you comprehend how the model responds to various inputs and letting you fine-tune your prompts for ideal results.<br>
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<br>You can rapidly test the model in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require 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 demonstrates how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends out a demand to create 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 is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient methods: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you select the technique that finest fits your needs.<br>
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<br>Deploy DeepSeek-R1 through [SageMaker JumpStart](http://okosg.co.kr) 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, select Studio in the navigation pane.
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2. First-time users will be triggered to [produce](https://jobs.assist-staffing.com) 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 web browser shows available designs, with details like the provider name and design abilities.<br>
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
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Each model card reveals key details, including:<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), indicating that this design can be registered with Amazon Bedrock, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) allowing you to utilize Amazon Bedrock APIs to conjure up the design<br>
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<br>5. Choose the model card to see the model 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 service provider details.
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Deploy button to deploy the design.
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About and Notebooks tabs with detailed details<br>
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<br>The About tab consists of 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 guidelines<br>
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<br>Before you deploy the design, it's recommended to examine the model details and license terms to validate compatibility with your use case.<br>
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<br>6. Choose Deploy to continue with implementation.<br>
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<br>7. For Endpoint name, use the automatically created name or produce a custom-made one.
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8. For example [type ¸](https://arbeitswerk-premium.de) select a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial circumstances count, get in the number of circumstances (default: 1).
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Selecting appropriate circumstances types and counts is important for expense and efficiency optimization. [Monitor](https://www.dpfremovalnottingham.com) your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
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10. Review all configurations for precision. For this model, we strongly recommend adhering to SageMaker JumpStart 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 implementation procedure can take numerous minutes to complete.<br>
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<br>When implementation is total, your endpoint status will alter to InService. At this moment, the model is all set to accept reasoning requests through the [endpoint](https://vidy.africa). You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show [pertinent metrics](https://git.liubin.name) and status details. When the [implementation](https://www.ukdemolitionjobs.co.uk) is total, you can invoke the model using a SageMaker runtime client and incorporate 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 get begun with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1093372) utilize DeepSeek-R1 for [reasoning programmatically](https://feniciaett.com). The code for [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:SelenaBaylebridg) deploying the model is provided in the Github here. You can clone the notebook and run from [SageMaker Studio](https://www.opad.biz).<br>
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<br>You can run extra requests against the predictor:<br>
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<br>Implement guardrails and run [inference](https://www.pkjobs.store) 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 using the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br>
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<br>Tidy 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 deployment<br>
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<br>If you released the design using Amazon Bedrock Marketplace, [raovatonline.org](https://raovatonline.org/author/giagannon42/) total the following actions:<br>
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
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2. In the Managed releases area, find the endpoint you wish to erase.
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3. Select the endpoint, and on the Actions menu, select Delete.
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4. Verify the endpoint details to make certain you're deleting the correct implementation: 1. Endpoint name.
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2. Model name.
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3. Endpoint status<br>
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<br>Delete the SageMaker JumpStart predictor<br>
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<br>The SageMaker JumpStart model you released will [sustain costs](https://jvptube.net) 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:AndreThorp2) 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 going with [Amazon SageMaker](https://culturaitaliana.org) 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://git.kitgxrl.gay) business develop innovative solutions using AWS services and accelerated compute. Currently, he is concentrated on developing techniques for [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:HallieBoothe4) fine-tuning and enhancing the reasoning efficiency of large [language](https://www.cupidhive.com) models. In his spare time, Vivek enjoys hiking, enjoying motion pictures, and [attempting](http://123.60.19.2038088) various foods.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.goatwu.com) Specialist Solutions Architect with the [Third-Party Model](http://valueadd.kr) Science team at AWS. His area of focus is AWS [AI](https://property.listatto.ca) 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](http://gitlab.andorsoft.ad) with the Third-Party Model Science group at AWS.<br>
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<br>[Banu Nagasundaram](https://xajhuang.com3100) leads product, engineering, and [tactical collaborations](https://git.whitedwarf.me) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://play.uchur.ru) center. She is enthusiastic about developing services that assist [clients accelerate](http://120.78.74.943000) their [AI](https://www.jobassembly.com) journey and unlock company worth.<br>
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