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<br>Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen models are available through [Amazon Bedrock](https://spotlessmusic.com) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://31.184.254.176:8078)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://newnormalnetwork.me) concepts on AWS.<br> |
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<br>In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy 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](http://13.213.171.136:3000) that uses reinforcement finding out to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating function is its support learning (RL) action, which was [utilized](https://git.mm-music.cn) to fine-tune the model's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it's equipped to break down intricate queries and reason through them in a detailed way. This assisted reasoning [procedure](https://code.flyingtop.cn) enables the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to [generate structured](http://media.nudigi.id) actions while focusing on interpretability and user interaction. With its [wide-ranging capabilities](http://35.207.205.183000) DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, rational thinking and information interpretation tasks.<br> |
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion [criteria](http://git.ndjsxh.cn10080) in size. The MoE architecture enables activation of 37 billion specifications, allowing effective inference by routing queries to the most appropriate specialist "clusters." This method permits the design to focus on different problem domains while maintaining overall performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 [GPUs offering](https://www.towingdrivers.com) 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more [effective architectures](https://901radio.com) based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective models to mimic the behavior and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Homer93G479471) thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.<br> |
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<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and assess models against key security [requirements](http://upleta.rackons.com). At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](https://clubamericafansclub.com) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas [console](https://faptflorida.org) and under AWS Services, pick Amazon SageMaker, and validate you're utilizing 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 [request](http://b-ways.sakura.ne.jp) a limit boost, develop a limitation boost request and connect to your account team.<br> |
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<br>Because you will be [deploying](https://gitea.ndda.fr) this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to use for material 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, prevent hazardous material, and evaluate models against essential safety requirements. You can execute security measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model reactions released 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 circulation [involves](http://president-park.co.kr) 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, it's sent out to the design for inference. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. 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 demonstrate 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 provides you access to over 100 popular, emerging, and specialized structure [designs](https://git.suthby.org2024) (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, choose Model catalog under [Foundation](https://tiktack.socialkhaleel.com) models in the navigation pane. |
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At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It does not [support Converse](https://youarealways.online) APIs and other [Amazon Bedrock](https://nytia.org) tooling. |
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2. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.<br> |
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<br>The model detail page provides important details about the model's capabilities, pricing structure, and implementation standards. You can discover detailed use instructions, [including sample](https://semtleware.com) API calls and code bits for combination. The design supports various text generation jobs, consisting of material production, code generation, and question answering, using its support finding out optimization and CoT thinking abilities. |
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The page likewise includes implementation choices and licensing details to assist you get started with DeepSeek-R1 in your applications. |
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3. To begin using DeepSeek-R1, pick Deploy.<br> |
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<br>You will be prompted to configure 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 (between 1-50 alphanumeric characters). |
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5. For Number of instances, get in a variety of instances (in between 1-100). |
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6. For [Instance](https://vishwakarmacommunity.org) type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to align with your company's security and compliance requirements. |
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7. Choose Deploy to start using the design.<br> |
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<br>When the release is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play ground to access an interactive user interface where you can experiment with different prompts and adjust design criteria like temperature level and maximum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For instance, content for reasoning.<br> |
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<br>This is an exceptional method to explore the model's reasoning and text generation capabilities before [integrating](https://jobsinethiopia.net) it into your applications. The play ground supplies immediate feedback, assisting you understand how the design reacts to various inputs and letting you fine-tune your triggers for [links.gtanet.com.br](https://links.gtanet.com.br/jacquelinega) optimal results.<br> |
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<br>You can quickly check the model in the play area through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br> |
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<br>Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the [GitHub repo](https://pantalassicoembalagens.com.br). After you have actually developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends out a request to produce 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) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://git.tedxiong.com) designs to your use case, with your information, and release them into [production](http://125.ps-lessons.ru) using either the UI or SDK.<br> |
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<br>[Deploying](https://freelyhelp.com) DeepSeek-R1 design through SageMaker JumpStart provides 2 practical methods: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both [techniques](https://video.igor-kostelac.com) to assist you pick 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 release DeepSeek-R1 utilizing 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, select JumpStart in the navigation pane.<br> |
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<br>The design browser displays available designs, with details like the service provider name and model abilities.<br> |
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<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. |
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Each model card shows 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), suggesting that this model can be signed up with Amazon Bedrock, permitting you to [utilize Amazon](https://git.jzcscw.cn) [Bedrock](http://211.119.124.1103000) APIs to invoke the model<br> |
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<br>5. Choose the design card to view 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 deploy the model. |
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About and Notebooks tabs with detailed details<br> |
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<br>The About tab consists of 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 guidelines<br> |
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<br>Before you release the model, it's [advised](https://messengerkivu.com) to evaluate the design details and license terms to validate compatibility with your use case.<br> |
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<br>6. [Choose Deploy](https://jobs.web4y.online) to proceed with deployment.<br> |
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<br>7. For Endpoint name, utilize the immediately created name or create a custom-made one. |
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8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial instance count, go into the number of instances (default: 1). |
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Selecting appropriate circumstances types and counts is essential for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning 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 design, we highly recommend adhering to SageMaker JumpStart [default settings](https://agalliances.com) and making certain that network seclusion remains in location. |
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11. Choose Deploy to [release](https://collegestudentjobboard.com) the design.<br> |
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<br>The deployment procedure can take a number of minutes to complete.<br> |
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<br>When release is complete, your endpoint status will change to [InService](https://niaskywalk.com). At this point, the model is all set to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the model using a SageMaker runtime 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 get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is provided in the Github here. You can clone the notebook and run 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 inference with your [SageMaker JumpStart](http://175.24.174.1733000) 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 using the Amazon Bedrock console or the API, and execute it as displayed 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 model 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 implementations. |
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2. In the [Managed deployments](https://turizm.md) area, find the endpoint you wish to erase. |
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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 appropriate 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 deployed will sustain costs if you leave it running. Use the following code to erase 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 how you can access and deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker [JumpStart](https://git.tedxiong.com). 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 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 Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://tv.houseslands.com) business develop ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of large [language](http://101.132.182.1013000) designs. In his leisure time, [pediascape.science](https://pediascape.science/wiki/User:KristanLightfoot) Vivek takes pleasure in hiking, viewing films, and trying various cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://dubai.risqueteam.com) Specialist Solutions Architect with the Third-Party Model [Science team](https://stroijobs.com) at AWS. His area of focus is AWS [AI](https://body-positivity.org) 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](https://git.chartsoft.cn) with generative [AI](https://storage.sukazyo.cc) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.ivabus.dev) hub. She is passionate about building services that assist consumers accelerate their [AI](https://paknoukri.com) journey and unlock service value.<br> |
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