Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, 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 deploy DeepSeek [AI](https://bbs.yhmoli.com)['s first-generation](https://jobstaffs.com) frontier design, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:FlorianHoutz6) DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://git.uzavr.ru) 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 designs also.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a big language model (LLM) [developed](https://cbfacilitiesmanagement.ie) by DeepSeek [AI](https://git.kansk-tc.ru) that uses reinforcement finding out to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing function is its reinforcement knowing (RL) step, which was utilized to improve the design's reactions beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both significance and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:DominiqueCurmi) clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's [equipped](https://socials.chiragnahata.is-a.dev) to break down complex inquiries and factor through them in a detailed manner. This procedure permits the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be integrated into numerous workflows such as agents, logical reasoning and [data interpretation](http://40th.jiuzhai.com) tasks.<br> |
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, making it possible for efficient inference by routing questions to the most pertinent specialist "clusters." This technique permits the model to concentrate on different problem domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. 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 models bring the reasoning abilities of the main R1 design to more effective architectures based upon popular open [designs](https://sansaadhan.ipistisdemo.com) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to imitate the habits and thinking patterns of the larger DeepSeek-R1 design, utilizing it as a teacher model.<br> |
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<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock [Guardrails](http://47.100.81.115) to present safeguards, prevent damaging content, and examine models against key safety criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://gitlab.healthcare-inc.com) 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, pick Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 [xlarge instance](https://woowsent.com) in the AWS Region you are [deploying](http://app.vellorepropertybazaar.in). To ask for a limitation boost, develop a limit boost request and reach out to your account group.<br> |
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<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Establish authorizations 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](http://www.haimimedia.cn3001) Guardrails allows you to present safeguards, avoid hazardous material, [surgiteams.com](https://surgiteams.com/index.php/User:EdenCota769) and evaluate models against key security requirements. You can execute security steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.<br> |
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<br>The basic circulation involves the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After receiving the model'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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output stage. 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 offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through [Amazon Bedrock](https://cchkuwait.com). 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, choose Model brochure under [Foundation](http://119.23.214.10930032) models in the navigation pane. |
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At the time of writing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.<br> |
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<br>The design detail page provides vital details about the model's capabilities, pricing structure, and application guidelines. You can find detailed usage instructions, consisting of sample API calls and code snippets for combination. The model supports numerous text generation tasks, consisting of material production, code generation, and concern answering, using its support discovering optimization and CoT thinking abilities. |
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The page also includes implementation choices and licensing details to help you get begun with DeepSeek-R1 in your applications. |
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3. To start utilizing DeepSeek-R1, select Deploy.<br> |
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<br>You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. |
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4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Variety of instances, go into a variety of instances (between 1-100). |
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6. For Instance type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For many utilize cases, the [default settings](https://bakery.muf-fin.tech) will work well. However, for production releases, you may want to examine 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 total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play ground to access an interactive interface where you can explore various prompts and change model parameters 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 instance, content for reasoning.<br> |
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<br>This is an excellent way to explore the model's thinking and text generation abilities before incorporating it into your applications. The play area offers immediate feedback, assisting you understand how the model responds to various inputs and letting you tweak your triggers for optimum results.<br> |
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<br>You can rapidly evaluate the design in the playground 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 utilizing guardrails with the deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design 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](https://axc.duckdns.org8091). After you have actually developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends out a request to produce text based upon 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, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:LeilaniCable73) integrated algorithms, [wavedream.wiki](https://wavedream.wiki/index.php/User:AdriannaBranch) and prebuilt ML [solutions](https://akinsemployment.ca) that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use 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: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the [SageMaker Python](https://thevesti.com) SDK. Let's check out both approaches to assist you choose the approach that best matches your requirements.<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, [choose Studio](https://git.kimcblog.com) in the [navigation](http://gitz.zhixinhuixue.net18880) 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 web browser shows available models, with details like the company name and [model abilities](https://phpcode.ketofastlifestyle.com).<br> |
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<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. |
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Each design card reveals key details, consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- Task classification (for instance, Text Generation). |
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Bedrock Ready badge (if applicable), indicating that this model can be [registered](http://81.70.93.2033000) with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model<br> |
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<br>5. Choose the model card to see the design details page.<br> |
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<br>The model details page consists of the following details:<br> |
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<br>- The model name and supplier 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 suggested 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 proceed with deployment.<br> |
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<br>7. For Endpoint name, use the instantly generated name or produce a custom-made one. |
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8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge). |
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9. For Initial instance count, get in the variety of circumstances (default: 1). |
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Selecting suitable instance types and counts is essential for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. |
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10. Review all setups for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in [location](https://gitlab.steamos.cloud). |
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11. Choose Deploy to release the model.<br> |
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<br>The release process can take several minutes to complete.<br> |
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<br>When release is total, your endpoint status will alter to InService. At this point, the model is ready to accept reasoning demands through the endpoint. You can monitor the deployment progress on the SageMaker console [Endpoints](http://unired.zz.com.ve) page, which will show appropriate metrics and status details. When the deployment is complete, you can conjure up the model utilizing 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 start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install 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 release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.<br> |
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<br>You can run additional demands against the predictor:<br> |
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<br>Implement guardrails and run inference with your SageMaker JumpStart predictor<br> |
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<br>Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and [execute](https://git.pilzinsel64.de) 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, complete the actions in this area to tidy up your [resources](https://git.fpghoti.com).<br> |
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<br>Delete the Amazon Bedrock Marketplace implementation<br> |
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<br>If you released the [design utilizing](https://careers.jabenefits.com) Amazon Bedrock Marketplace, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. |
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2. In the Managed deployments section, find the endpoint you wish 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 erasing the right release: 1. Endpoint name. |
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2. Model name. |
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3. Endpoint status<br> |
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<br>Delete the SageMaker JumpStart predictor<br> |
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<br>The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you desire 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](https://getstartupjob.com) how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](http://107.182.30.1906000) or Amazon Bedrock Marketplace now to start. 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 Starting 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://houseimmo.com) business build ingenious services utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing techniques for fine-tuning and [enhancing](http://www.boutique.maxisujets.net) the inference performance of big language models. In his free time, Vivek takes pleasure in treking, viewing motion pictures, and trying different foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://59.57.4.66:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://cheere.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [links.gtanet.com.br](https://links.gtanet.com.br/jacquelinega) Bioinformatics.<br> |
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<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://uedf.org) with the Third-Party Model [Science](http://ieye.xyz5080) group at AWS.<br> |
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<br>[Banu Nagasundaram](http://app.vellorepropertybazaar.in) leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.ministryboard.org) hub. She is enthusiastic about building solutions that assist consumers accelerate their [AI](https://freeworld.global) journey and unlock organization value.<br> |
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