Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://84.247.150.84:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled [versions varying](https://www.soundofrecovery.org) from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://uniondaocoop.com) concepts 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 similar steps to deploy the distilled variations of the models also.<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 by DeepSeek [AI](https://git.agent-based.cn) that utilizes reinforcement finding out to boost thinking [abilities](https://gitea.johannes-hegele.de) through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its reinforcement learning (RL) step, which was utilized to fine-tune the design's responses beyond the standard pre-training and [tweak process](https://bug-bounty.firwal.com). By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually improving both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's equipped to break down complex questions and factor through them in a detailed way. This directed thinking process enables the design to produce more accurate, transparent, and detailed answers. This design combines RL-based [fine-tuning](https://social.midnightdreamsreborns.com) with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its [comprehensive capabilities](https://linkin.commoners.in) DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be [integrated](https://tjoobloom.com) into various workflows such as representatives, sensible reasoning and information analysis tasks.<br> |
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Joshua8632) enabling efficient inference by routing questions to the most pertinent professional "clusters." This method enables the model to specialize in various issue domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 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 efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient designs to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a [teacher design](https://wiki.sublab.net).<br> |
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<br>You can release DeepSeek-R1 model either through [SageMaker JumpStart](http://47.112.200.2063000) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this design with guardrails in location. In this blog site, we will utilize Amazon [Bedrock](https://www.calogis.com) Guardrails to introduce safeguards, prevent hazardous material, and examine models against crucial safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://git.tea-assets.com) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To request a limitation boost, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:ClaudetteYwg) create a limit boost demand and reach out to your account team.<br> |
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<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Set up permissions 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, avoid harmful material, and examine designs against essential safety criteria. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to [evaluate](http://www.heart-hotel.com) user inputs and model responses released on [Amazon Bedrock](https://source.coderefinery.org) Marketplace and SageMaker JumpStart. You can create 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 circulation involves the following actions: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, [surgiteams.com](https://surgiteams.com/index.php/User:JunkoZ85423) it's sent out to the design for reasoning. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the final result. 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 [occurred](http://www.heart-hotel.com) at the input or output phase. The examples showcased in the following areas show reasoning using this API.<br> |
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br> |
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<br>Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. |
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At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a supplier and [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:RaphaelMorton32) pick the DeepSeek-R1 design.<br> |
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<br>The design detail page offers important [details](https://goodprice-tv.com) about the design's abilities, pricing structure, and implementation guidelines. You can discover detailed use directions, including sample API calls and code snippets for combination. The design supports various text generation tasks, consisting of content development, code generation, and question answering, utilizing its support discovering optimization and CoT reasoning abilities. |
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The page likewise consists of release alternatives and licensing details to assist you get started with DeepSeek-R1 in your applications. |
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3. To begin utilizing DeepSeek-R1, pick Deploy.<br> |
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<br>You will be prompted to set up the deployment details for DeepSeek-R1. The model 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 circumstances, enter a variety of instances (between 1-100). |
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6. For Instance type, pick your instance type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you might want to review these settings to line up with your organization's security and [compliance](http://122.51.6.973000) requirements. |
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7. Choose Deploy to start utilizing the model.<br> |
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<br>When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. |
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8. Choose Open in play area to access an interactive user interface where you can explore different triggers and change model parameters like temperature level and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for inference.<br> |
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<br>This is an outstanding way to explore the design's thinking and text generation capabilities before incorporating it into your applications. The playground provides instant feedback, assisting you comprehend how the design reacts to numerous inputs and letting you tweak your triggers for optimal outcomes.<br> |
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<br>You can rapidly check the design in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br> |
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<br>Run reasoning using guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to carry out reasoning using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the [Amazon Bedrock](http://121.36.62.315000) console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends a request to create [text based](https://gitea.lolumi.com) 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) center with FMs, built-in algorithms, and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1337957) prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into [production](https://git.gra.phite.ro) using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free techniques: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you select the method that best matches your [requirements](https://www.lotusprotechnologies.com).<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, choose Studio in the navigation pane. |
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2. First-time users will be prompted to create a domain. |
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3. On the SageMaker Studio console, select JumpStart in the [navigation](https://gitea.rodaw.net) pane.<br> |
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<br>The model internet browser shows available designs, with details like the service provider name and model capabilities.<br> |
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<br>4. Search for DeepSeek-R1 to view 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 example, Text Generation). |
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Bedrock Ready badge (if appropriate), showing that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke 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 model details page consists of the following details:<br> |
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<br>- The model name and company details. |
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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 specifications. |
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[- Usage](http://git.bzgames.cn) guidelines<br> |
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<br>Before you deploy the model, it's suggested to evaluate the model details and license terms to verify compatibility with your use case.<br> |
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<br>6. Choose Deploy to proceed with release.<br> |
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<br>7. For Endpoint name, utilize the [instantly](https://gitea.nongnghiepso.com) created name or create a custom one. |
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8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge). |
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9. For [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:SheilaDaws73394) Initial circumstances count, go into the number of instances (default: 1). |
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Selecting appropriate [circumstances types](https://nemoserver.iict.bas.bg) and counts is crucial for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is selected by [default](https://git.vhdltool.com). This is optimized for [sustained traffic](http://hitq.segen.co.kr) and low latency. |
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10. Review all configurations for precision. For this model, we highly advise 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 model.<br> |
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<br>The implementation process can take numerous minutes to finish.<br> |
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<br>When is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is complete, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your [applications](https://git.elder-geek.net).<br> |
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<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br> |
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<br>To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will [require](https://almagigster.com) to set up 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 deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design 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 additional demands 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 utilize 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>Clean up<br> |
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<br>To avoid unwanted charges, complete the actions in this section to clean 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, total the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. |
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2. In the Managed releases section, locate the endpoint you want to delete. |
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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 deleting the right deployment: 1. Endpoint name. |
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2. Model name. |
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3. Endpoint status<br> |
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<br>Delete the SageMaker JumpStart predictor<br> |
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<br>The SageMaker JumpStart model you released will sustain costs if you leave it running. Use the following code to delete 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](https://music.michaelmknight.com) 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 started. 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 Getting going 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 helps emerging generative [AI](http://www.raverecruiter.com) companies develop innovative services using AWS services and sped up calculate. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the [reasoning efficiency](https://doop.africa) of big language models. In his spare time, Vivek enjoys hiking, viewing films, and [attempting](http://39.106.8.2463003) different foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://gitlab.wah.ph) [Specialist Solutions](http://39.108.83.1543000) Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://wdz.imix7.com:13131) accelerators (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://lifestagescs.com) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://visorus.com.mx) hub. She is passionate about constructing solutions that help consumers accelerate their [AI](https://www.allgovtjobz.pk) journey and unlock organization value.<br> |
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