Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are delighted to announce that [DeepSeek](https://jobsantigua.com) R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now [deploy DeepSeek](http://47.100.17.114) [AI](https://mychampionssport.jubelio.store)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](https://www.activeline.com.au) ideas on AWS.<br> |
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<br>In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can [follow comparable](https://bytevidmusic.com) actions to deploy the distilled variations of the models too.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](http://47.103.91.160:50903) that uses reinforcement discovering to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating feature is its reinforcement learning (RL) step, which was utilized to fine-tune the model's reactions beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's equipped to break down [intricate inquiries](https://gogs.artapp.cn) and reason through them in a detailed way. This assisted reasoning procedure enables the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be incorporated into numerous workflows such as agents, sensible thinking and data analysis jobs.<br> |
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, allowing efficient inference by routing queries to the most relevant specialist "clusters." This technique allows the model to focus on various issue domains while maintaining total efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more effective architectures based upon popular open [designs](https://social1776.com) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to simulate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an [instructor design](http://38.12.46.843333).<br> |
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<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and evaluate models against essential safety criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, [pediascape.science](https://pediascape.science/wiki/User:Jeremy54P355271) Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://nusalancer.netnation.my.id) applications.<br> |
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<br>Prerequisites<br> |
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<br>To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the [Service Quotas](https://cvwala.com) 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 circumstances in the AWS Region you are deploying. To request a limit 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 utilize Amazon Bedrock Guardrails. For guidelines, see Establish authorizations 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](https://gitea.createk.pe) Guardrails [enables](https://gogs.yaoxiangedu.com) you to introduce safeguards, prevent hazardous content, and [assess models](https://git.dadunode.com) against crucial security requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon [Bedrock console](http://47.97.161.14010080) or the API. For the example code to develop the guardrail, see the GitHub repo.<br> |
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<br>The general flow includes the following steps: First, the system receives an input for the design. This input is then processed through the [ApplyGuardrail API](https://farmwoo.com). If the input passes the guardrail check, it's sent to the model for inference. After receiving the model's output, another guardrail check is used. 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 suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections demonstrate inference utilizing this API.<br> |
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<br>Deploy DeepSeek-R1 in [Amazon Bedrock](https://gogocambo.com) Marketplace<br> |
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<br>Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure designs (FMs) through [Amazon Bedrock](https://ec2-13-237-50-115.ap-southeast-2.compute.amazonaws.com). 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 brochure under Foundation designs in the navigation pane. |
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At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a [company](https://tjoobloom.com) and select the DeepSeek-R1 design.<br> |
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<br>The model detail page provides essential details about the model's capabilities, rates structure, and application guidelines. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The model supports different text generation jobs, consisting of content production, code generation, and question answering, utilizing its support finding out optimization and CoT thinking abilities. |
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The page also consists of deployment alternatives and licensing details to assist you start 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 set up 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 (between 1-50 alphanumeric characters). |
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5. For Number of instances, get in a number of circumstances (in between 1-100). |
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6. For Instance type, select your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can configure sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service role permissions, and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11862161) encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you may want to examine these settings to line up with your organization's security and compliance requirements. |
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7. Choose Deploy to begin using the design.<br> |
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<br>When the implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play area to access an interactive interface where you can explore different triggers and adjust design criteria like temperature and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, content for reasoning.<br> |
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<br>This is an outstanding way to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The play area offers immediate feedback, [helping](https://nationalcarerecruitment.com.au) you comprehend how the model reacts to various inputs and letting you tweak your prompts for optimal outcomes.<br> |
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<br>You can rapidly test the design in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br> |
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<br>Run [inference](https://namoshkar.com) utilizing guardrails with the DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MichelleHarmer9) see the GitHub repo. After you have actually produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, [configures reasoning](http://jenkins.stormindgames.com) criteria, and sends a demand to produce 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) hub with FMs, built-in algorithms, [surgiteams.com](https://surgiteams.com/index.php/User:LaureneDortch1) and prebuilt ML options that you can [release](https://medicalstaffinghub.com) with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](https://rhcstaffing.com) 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 provides 2 practical techniques: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to help 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 release DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, [yewiki.org](https://www.yewiki.org/User:ElenaGrenda45) select 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, choose JumpStart in the navigation pane.<br> |
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<br>The design browser shows available designs, with details like the company name and design abilities.<br> |
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. |
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Each design card reveals crucial details, consisting of:<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 appropriate), showing that this design can be signed up with Amazon Bedrock, enabling 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 design details page.<br> |
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<br>The model details page includes the following details:<br> |
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<br>- The model name and [provider details](https://betalk.in.th). |
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Deploy button to release the model. |
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About and Notebooks tabs with detailed details<br> |
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<br>The About tab includes essential details, such as:<br> |
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<br>- Model description. |
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- License details. |
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- Technical requirements. |
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- Usage guidelines<br> |
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<br>Before you release the model, it's suggested to review the model details and license terms to confirm 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 immediately generated name or create 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 circumstances count, go into the number of circumstances (default: 1). |
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Selecting proper instance types and counts is crucial for expense and efficiency optimization. Monitor your release to change these [settings](http://company-bf.com) as needed.Under Inference type, Real-time reasoning is picked by [default](http://211.117.60.153000). This is [optimized](https://hr-2b.su) for sustained traffic and low latency. |
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10. Review all setups for accuracy. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. |
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11. Choose Deploy to deploy the design.<br> |
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<br>The deployment process can take a number of minutes to complete.<br> |
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<br>When deployment is complete, your [endpoint status](http://gungang.kr) will alter to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and [status details](https://saghurojobs.com). When the [release](https://tapeway.com) is complete, you can invoke the design using a SageMaker runtime customer 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 begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing 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 requests against the predictor:<br> |
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<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br> |
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<br>Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:<br> |
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<br>Tidy up<br> |
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<br>To prevent undesirable charges, complete the steps in this area to tidy up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace release<br> |
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<br>If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments. |
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2. In the Managed releases area, locate the endpoint you desire 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 appropriate 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 design you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For [garagesale.es](https://www.garagesale.es/author/randieberly/) more details, see Delete Endpoints and 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 deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. 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 designs, [SageMaker JumpStart](http://51.79.251.2488080) pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with [Amazon SageMaker](http://mengqin.xyz3000) JumpStart.<br> |
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<br>About the Authors<br> |
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<br>[Vivek Gangasani](https://ka4nem.ru) is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://jamesrodriguezclub.com) business develop ingenious solutions utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the reasoning efficiency of large language models. In his leisure time, Vivek delights in treking, watching films, and attempting different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://vimalakirti.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://somkenjobs.com) [accelerators](http://113.177.27.2002033) (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 dealing with generative [AI](https://git.alexhill.org) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and [surgiteams.com](https://surgiteams.com/index.php/User:Bradford7526) tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://bolsatrabajo.cusur.udg.mx) hub. She is passionate about developing services that assist consumers accelerate their [AI](https://git.mario-aichinger.com) journey and unlock business worth.<br> |
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