Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart
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DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md
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DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md
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<br>Today, we are thrilled 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://archie2429263902267.bloggersdelight.dk)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://git.lazyka.ru) ideas on AWS.<br>
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<br>In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions 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 design (LLM) established by DeepSeek [AI](https://acrohani-ta.com) that utilizes reinforcement learning to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing function is its support learning (RL) action, which was used to refine the model's responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's geared up to break down complicated inquiries and reason through them in a [detailed](http://8.139.7.16610880) way. This guided reasoning procedure enables the design to produce more precise, transparent, and detailed answers. This model combines RL-based [fine-tuning](http://222.121.60.403000) with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its [extensive capabilities](http://111.9.47.10510244) DeepSeek-R1 has caught the market's attention as a flexible text-generation design that can be integrated into various workflows such as agents, sensible thinking and information interpretation jobs.<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](https://visualchemy.gallery) activation of 37 billion specifications, allowing effective inference by routing queries to the most appropriate expert "clusters." This approach permits the model to focus on various issue domains while maintaining total effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will [utilize](https://kohentv.flixsterz.com) an ml.p5e.48 [xlarge instance](https://www.elitistpro.com) to deploy the design. 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 effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient models to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher model.<br>
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<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and examine designs against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 design, [improving](http://g-friend.co.kr) user experiences and standardizing safety controls across your generative [AI](https://supremecarelink.com) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm 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 ask for a limit increase, create a limitation boost request and connect to your account team.<br>
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<br>Because you will be [deploying](https://git.haowumc.com) this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Set up approvals 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 enables you to introduce safeguards, avoid damaging material, and evaluate models against key security criteria. You can execute security steps for the DeepSeek-R1 design utilizing the [Amazon Bedrock](https://nycu.linebot.testing.jp.ngrok.io) ApplyGuardrail API. This permits you to use guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.<br>
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<br>The general circulation 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 check, it's sent to the model for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the 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 at the input or output phase. The examples showcased in the following areas show inference utilizing this API.<br>
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
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<br>Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure 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, choose Model brochure under Foundation models in the navigation pane.
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At the time of writing this post, you can [utilize](https://wiki.vifm.info) the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.<br>
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<br>The model detail page offers necessary details about the model's abilities, prices structure, and execution guidelines. You can find detailed usage guidelines, consisting of sample API calls and code bits for combination. The model supports numerous text generation tasks, [consisting](https://git.nosharpdistinction.com) of content development, code generation, and question answering, utilizing its reinforcement learning optimization and CoT reasoning capabilities.
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The page also consists of implementation alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications.
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3. To start utilizing DeepSeek-R1, choose Deploy.<br>
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<br>You will be prompted to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated.
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4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
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5. For [Variety](http://awonaesthetic.co.kr) of instances, get in a variety of circumstances (between 1-100).
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6. For Instance type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
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Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you may want to evaluate these [settings](https://cbfacilitiesmanagement.ie) to line up with your company'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 deployment is total, you can evaluate 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 interface where you can try out various prompts and adjust model specifications like temperature level and maximum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, material for reasoning.<br>
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<br>This is an outstanding method to explore the design's thinking and text [generation capabilities](http://114.34.163.1743333) before integrating it into your applications. The play ground provides instant feedback, helping you comprehend how the design reacts to various inputs and you tweak your [triggers](https://denis.usj.es) for ideal results.<br>
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<br>You can quickly test the design in the playground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
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<br>Run inference using guardrails with the released DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to perform reasoning [utilizing](https://git.wo.ai) a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock [console](https://okoskalyha.hu) or the API. For the example code to produce the guardrail, see the [GitHub repo](https://luckyway7.com). After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_[runtime](https://www.jobassembly.com) customer, configures reasoning criteria, and sends out a demand to generate text based on 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) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into [production](https://allcollars.com) using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the method that best fits your needs.<br>
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
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<br>Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:<br>
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<br>1. On the SageMaker console, pick Studio 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, pick JumpStart in the navigation pane.<br>
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<br>The model internet browser shows available designs, with [details](https://www.hrdemployment.com) like the [supplier](http://euhope.com) name and design abilities.<br>
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<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
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Each design card reveals crucial details, including:<br>
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<br>- Model name
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- Provider name
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- Task classification (for instance, Text Generation).
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Bedrock Ready badge (if appropriate), indicating that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model<br>
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<br>5. Choose the design card to see the model details page.<br>
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<br>The design details page consists of the following details:<br>
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<br>- The design name and supplier details.
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Deploy button to deploy the model.
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About and [disgaeawiki.info](https://disgaeawiki.info/index.php/User:MorrisSherrill8) Notebooks tabs with detailed details<br>
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<br>The About tab consists of crucial details, such as:<br>
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<br>- Model description.
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- License details.
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- Technical specifications.
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- Usage guidelines<br>
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<br>Before you deploy the design, it's recommended to examine the model details and license terms to confirm compatibility with your usage case.<br>
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<br>6. Choose Deploy to proceed with deployment.<br>
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<br>7. For Endpoint name, use the instantly produced name or create a custom one.
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8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial circumstances count, enter the variety of instances (default: 1).
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Selecting appropriate instance types and counts is essential for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is chosen by [default](https://gitlab.cloud.bjewaytek.com). This is enhanced for sustained traffic and low latency.
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10. Review all configurations for [accuracy](https://wino.org.pl). For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
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11. Choose Deploy to deploy the design.<br>
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<br>The implementation procedure can take numerous minutes to finish.<br>
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<br>When deployment is complete, your endpoint status will alter to [InService](http://dimarecruitment.co.uk). At this moment, the design is all set to accept reasoning requests through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and [status details](https://clousound.com). When the release is total, you can conjure up the design utilizing a SageMaker runtime client and 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 started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker [Python SDK](http://kyeongsan.co.kr) and make certain you have the [required](https://activitypub.software) AWS [permissions](https://tayseerconsultants.com) and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is [offered](https://admithel.com) in the Github here. You can clone the notebook and run from SageMaker Studio.<br>
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<br>You can run [extra requests](https://inicknet.com) 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 utilize the ApplyGuardrail API with your [SageMaker JumpStart](https://gitea.malloc.hackerbots.net) predictor. You can produce a guardrail using the [Amazon Bedrock](http://carpediem.so30000) 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 avoid undesirable charges, complete the steps in this area to tidy up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace deployment<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 console, under Foundation models in the navigation pane, pick Marketplace deployments.
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2. In the Managed releases section, locate the [endpoint](https://www.bisshogram.com) you want to delete.
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3. Select the endpoint, and on the Actions menu, choose Delete.
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4. Verify the endpoint details to make certain you're erasing 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 expenses if you leave it [running](http://124.222.7.1803000). Use the following code to erase the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
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<br>Conclusion<br>
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<br>In this post, we explored how you can access and deploy the DeepSeek-R1 model utilizing Bedrock [Marketplace](http://119.3.70.2075690) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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](https://meetcupid.in) Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://www.globalshowup.com) business develop ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference performance of big [language](https://gitea.adminakademia.pl) models. In his leisure time, Vivek enjoys treking, watching movies, and attempting different cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://47.119.20.13:8300) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://dev.shopraves.com) 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](http://dibodating.com) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://thaisfriendly.com) center. She is enthusiastic about building solutions that assist consumers accelerate their [AI](https://media.izandu.com) journey and unlock company worth.<br>
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