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://lafffrica.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://hiphopmusique.com) ideas on AWS.<br> |
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<br>In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release 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) [established](http://101.43.135.2349211) by DeepSeek [AI](http://www.jimtangyh.xyz:7002) that uses [reinforcement learning](https://forum.webmark.com.tr) to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing feature is its support knowing (RL) action, which was used to refine the design's reactions beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, implying it's equipped to break down intricate inquiries and reason through them in a detailed way. This directed reasoning procedure enables the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation model that can be integrated into numerous workflows such as representatives, logical reasoning and information interpretation tasks.<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 enables activation of 37 billion specifications, enabling efficient inference by routing queries to the most pertinent expert "clusters." This technique permits the model to focus on different issue domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of [HBM memory](http://git.ai-robotics.cn) in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release 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 capabilities of the main R1 model to more efficient architectures based on popular open [designs](https://fototik.com) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to imitate the behavior and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1108898) reasoning patterns of the larger DeepSeek-R1 model, 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 model, we advise deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and examine designs against key safety criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://git.7vbc.com) only the ApplyGuardrail API. You can [produce multiple](https://eet3122salainf.sytes.net) guardrails tailored to various usage cases and [links.gtanet.com.br](https://links.gtanet.com.br/zarakda51931) use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://git.becks-web.de) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1090091) 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 in the AWS Region you are releasing. To ask for a limit boost, develop a limit increase demand and reach out 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 appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish authorizations to use 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 allows you to present safeguards, prevent hazardous content, and evaluate models against [crucial security](https://ou812chat.com) criteria. You can execute safety steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply 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 produce the guardrail, see the GitHub repo.<br> |
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<br>The basic flow includes the following steps: 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 out to the design for reasoning. After getting the model's output, another guardrail check is [applied](https://gitea.fcliu.net). If the output passes this final check, it's [returned](https://git.goatwu.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 took place at the input or output stage. The examples showcased in the following areas demonstrate 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 structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, pick Model catalog 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 invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.<br> |
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<br>The design detail page provides essential details about the model's abilities, prices structure, and execution standards. You can find detailed use instructions, consisting of sample API calls and code snippets for integration. The model supports various text generation jobs, consisting of material production, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking capabilities. |
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The page also consists of deployment options and licensing details to assist you start with DeepSeek-R1 in your [applications](https://sublimejobs.co.za). |
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3. To start utilizing DeepSeek-R1, choose Deploy.<br> |
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<br>You will be prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. |
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4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Number of circumstances, enter a variety of instances (between 1-100). |
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6. For example type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can set up sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, [service role](https://www.videomixplay.com) approvals, and file encryption settings. For most use cases, the default settings will work well. However, for production releases, [raovatonline.org](https://raovatonline.org/author/elanacaznea/) you may desire to evaluate these [settings](http://119.3.29.1773000) to line up with your organization's security and compliance requirements. |
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7. Choose Deploy to begin utilizing the design.<br> |
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<br>When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play area to access an interactive user interface where you can explore various prompts and adjust design parameters like temperature level and maximum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, content for inference.<br> |
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<br>This is an outstanding way to check out the model's thinking and text generation abilities before integrating it into your applications. The playground offers immediate feedback, assisting you [comprehend](http://git.cxhy.cn) how the model reacts to various inputs and letting you tweak your triggers for ideal results.<br> |
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<br>You can rapidly evaluate the model in the playground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br> |
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<br>Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to carry out inference using a deployed DeepSeek-R1 model 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 produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends a demand to [generate text](https://www.eticalavoro.it) based on 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) hub with FMs, built-in algorithms, and prebuilt ML services 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 utilizing either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 [hassle-free](http://101.42.41.2543000) techniques: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the [technique](https://men7ty.com) that best suits your needs.<br> |
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<br>Deploy DeepSeek-R1 through [SageMaker JumpStart](https://www.ahrs.al) 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](https://bdenc.com) in the navigation pane. |
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2. First-time users will be triggered 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 internet browser shows available models, with details like the service provider name and model abilities.<br> |
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. |
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Each design card shows essential 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](http://www.hxgc-tech.com3000) badge (if appropriate), showing that this model can be signed up 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 view the design 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 release 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](https://iesoundtrack.tv) description. |
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- License [details](https://git.citpb.ru). |
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- Technical specifications. |
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- Usage standards<br> |
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<br>Before you release the design, it's [advised](https://www.megahiring.com) to examine the design details and license terms to confirm compatibility with your usage case.<br> |
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<br>6. Choose Deploy to continue with implementation.<br> |
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<br>7. For Endpoint name, utilize the automatically generated name or produce a customized one. |
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8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, go into the number of instances (default: 1). |
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Selecting suitable circumstances types and counts is important for cost and efficiency optimization. Monitor your deployment to adjust these [settings](https://social.japrime.id) as needed.Under Inference type, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MeganShull47) Real-time reasoning is selected by default. This is optimized for sustained traffic and [low latency](http://git.appedu.com.tw3080). |
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10. Review all setups for [accuracy](https://navar.live). For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. |
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11. Choose Deploy to release the design.<br> |
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<br>The deployment procedure can take numerous minutes to finish.<br> |
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<br>When deployment is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is total, you can conjure up the model using a [SageMaker runtime](https://quickservicesrecruits.com) customer and integrate 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 start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker [Python SDK](https://dev.ncot.uk) and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for [deploying](http://git.guandanmaster.com) the model is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.<br> |
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<br>You can run extra 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:<br> |
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<br>Tidy up<br> |
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<br>To prevent undesirable charges, complete the actions in this section 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 deployed the design utilizing Amazon Bedrock Marketplace, complete the following steps:<br> |
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<br>1. On the [Amazon Bedrock](https://gitea.lolumi.com) console, under [Foundation designs](https://git.becks-web.de) in the navigation pane, pick Marketplace implementations. |
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2. In the Managed releases area, find the endpoint you want 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 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 expenses if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and [Resources](https://git.aaronmanning.net).<br> |
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<br>Conclusion<br> |
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<br>In this post, we checked out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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://my-estro.it) companies develop ingenious solutions using and sped up calculate. Currently, he is focused on developing methods for fine-tuning and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:MelvinRigg86964) optimizing the inference efficiency of large language models. In his free time, Vivek delights in hiking, watching movies, and trying different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://49.235.147.88:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://git.spitkov.hu) [accelerators](https://www.arztsucheonline.de) (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 dealing with generative [AI](https://yes.youkandoit.com) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.suyun.store) hub. She is passionate about constructing solutions that help consumers accelerate their [AI](http://freeflashgamesnow.com) journey and unlock business value.<br> |
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