Add '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 R1 distilled Llama and Qwen models are available through [Amazon Bedrock](http://gitlab.iyunfish.com) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://www.2dudesandalaptop.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative [AI](https://www.liveactionzone.com) concepts 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 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 big language model (LLM) established by DeepSeek [AI](https://ou812chat.com) that uses reinforcement discovering to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating function is its reinforcement knowing (RL) action, which was used to refine the design's responses beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, [implying](http://106.227.68.1873000) it's geared up to break down complex questions and factor through them in a detailed manner. This assisted [thinking procedure](http://185.5.54.226) allows the model to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation model that can be incorporated into various workflows such as agents, sensible reasoning and information interpretation tasks.<br> |
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, enabling efficient inference by routing queries to the most relevant specialist "clusters." This [method permits](http://162.55.45.543000) the design to concentrate on various issue domains while maintaining total effectiveness. DeepSeek-R1 needs 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 release the model. ml.p5e.48 xlarge includes 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 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to mimic the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.<br> |
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<br>You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with [guardrails](https://openedu.com) in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and [assess designs](https://gitoa.ru) against essential safety criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](https://lastpiece.co.kr) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 model, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:DinaHolcomb495) you need access to an ml.p5e [circumstances](https://git.qiucl.cn). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 deploying. To ask for a limit boost, create a limit boost demand and reach out to your account team.<br> |
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see Establish consents to utilize guardrails for content filtering.<br> |
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<br>Implementing guardrails with the ApplyGuardrail API<br> |
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<br>Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful material, and evaluate models against key safety requirements. You can carry out security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use [guardrails](http://git.365zuoye.com) to assess user inputs and model responses 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 create the guardrail, see the GitHub repo.<br> |
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<br>The basic flow includes 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](https://www.hirecybers.com) check, it's sent out to the design for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and [surgiteams.com](https://surgiteams.com/index.php/User:JamieBingaman8) whether it took place at the input or output phase. The examples showcased in the following areas show inference 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](https://sajano.com) provides you access to over 100 popular, emerging, and specialized foundation designs (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, [yewiki.org](https://www.yewiki.org/User:ElenaGrenda45) choose Model catalog under Foundation designs in the navigation pane. |
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At the time of composing this post, you can use the InvokeModel API to conjure up 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 model detail page offers essential details about the design's capabilities, rates structure, and application guidelines. You can find detailed use instructions, including sample API calls and code bits for combination. The [model supports](http://120.79.27.2323000) various text generation tasks, including material production, code generation, and [question](https://local.wuanwanghao.top3000) answering, using its support discovering optimization and CoT reasoning capabilities. |
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The page also includes implementation options and licensing details to help you get started with DeepSeek-R1 in your applications. |
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3. To start using DeepSeek-R1, [choose Deploy](https://pedulidigital.com).<br> |
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<br>You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated. |
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4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Number of circumstances, go into a [variety](http://103.235.16.813000) of instances (in between 1-100). |
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6. For Instance type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. |
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Optionally, you can configure sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function consents, and encryption settings. For most use cases, the default settings will work well. However, for production releases, you might wish to examine these settings to line up with your organization's security and compliance requirements. |
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7. Choose Deploy to begin [utilizing](https://www.locumsanesthesia.com) the model.<br> |
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<br>When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. |
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8. Choose Open in play ground to access an interactive user interface where you can try out different prompts and change design specifications like temperature level and optimum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For instance, content for inference.<br> |
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<br>This is an exceptional method to explore the model's thinking and text generation abilities before integrating it into your applications. The play ground offers instant feedback, assisting you comprehend how the design responds to various inputs and letting you tweak your triggers for ideal results.<br> |
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<br>You can quickly test the model in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require 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 demonstrates how to [perform inference](http://www.hnyqy.net3000) using a [released](https://www.seekbetter.careers) DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends a [request](https://git.jzmoon.com) to create [text based](https://wavedream.wiki) on a user timely.<br> |
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<br>Deploy DeepSeek-R1 with [SageMaker](http://89.234.183.973000) 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 [release](https://propbuysells.com) with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into [production utilizing](https://git.tx.pl) either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient methods: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker [Python SDK](https://git.smartenergi.org). Let's explore both approaches to assist you choose the technique that best matches your needs.<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, pick Studio in the navigation pane. |
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2. First-time users will be prompted to produce 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 model web browser displays available models, with details like the supplier name and design abilities.<br> |
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. |
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Each model card shows crucial details, consisting of:<br> |
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<br>- Model name |
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- [Provider](https://privat-kjopmannskjaer.jimmyb.nl) name |
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- Task classification (for example, Text Generation). |
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Bedrock Ready badge (if suitable), indicating that this model can be registered with Amazon Bedrock, enabling you to [utilize Amazon](https://lastpiece.co.kr) Bedrock APIs to invoke the model<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](http://59.57.4.663000) 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 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 model, it's recommended to evaluate the model details and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1322040) license terms to verify 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, utilize the [instantly produced](https://git.tasu.ventures) name or create a custom-made one. |
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8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge). |
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9. For Initial instance count, enter the variety of instances (default: 1). |
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Selecting proper circumstances types and counts is vital for cost and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. |
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10. Review all configurations for accuracy. For this model, we highly recommend 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 [release procedure](https://givebackabroad.org) can take numerous minutes to finish.<br> |
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<br>When implementation is total, your endpoint status will alter to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the model using a SageMaker runtime customer and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:XXBCorine49) incorporate 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 get going with DeepSeek-R1 using the [SageMaker Python](https://bertlierecruitment.co.za) SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS permissions and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:KennethBeals) environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is provided in the Github here. You can clone the note pad and range from SageMaker Studio.<br> |
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<br>You can run extra demands against the predictor:<br> |
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<br>Implement guardrails and run reasoning with your [SageMaker JumpStart](http://moyora.today) 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 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>Clean up<br> |
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<br>To avoid [undesirable](https://gitlab-heg.sh1.hidora.com) charges, finish 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 model using [Amazon Bedrock](https://coverzen.co.zw) 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 implementations. |
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2. In the Managed deployments area, locate the endpoint you desire to erase. |
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3. Select the endpoint, and on the Actions menu, select Delete. |
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4. Verify the endpoint details to make certain you're erasing the proper 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 design you deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br> |
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<br>Conclusion<br> |
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<br>In this post, we explored how you can access and [release](https://cello.cnu.ac.kr) the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. 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](https://www.jobzpakistan.info) at AWS. He assists emerging generative [AI](https://gitea.mpc-web.jp) [companies construct](https://www.tippy-t.com) ingenious solutions utilizing AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the reasoning efficiency of big language models. In his spare time, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:JuniorBowser22) Vivek enjoys hiking, watching movies, and trying different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://forum.ffmc59.fr) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://derivsocial.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://groups.chat) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://34.236.28.152) hub. She is enthusiastic about building options that assist clients accelerate their [AI](https://93.177.65.216) journey and unlock business value.<br> |
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