Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://101.33.255.60:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your [generative](http://git.scdxtc.cn) [AI](http://povoq.moe:1145) ideas on AWS.<br> |
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<br>In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs as well.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](http://47.106.205.140:8089) that utilizes reinforcement learning to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying function is its support learning (RL) step, which was utilized to [improve](https://likemochi.com) the model's reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, ultimately improving both importance and [clarity](http://jobjungle.co.za). In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, [implying](https://jandlfabricating.com) it's equipped to break down intricate questions and reason through them in a detailed way. This assisted reasoning process permits the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually [recorded](https://co2budget.nl) the market's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, logical thinking and data analysis 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 allows activation of 37 billion criteria, enabling efficient inference by routing questions to the most appropriate professional "clusters." This approach allows the design to focus on different issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for [inference](https://incomash.com). In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 [GPUs providing](https://coptr.digipres.org) 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 models bring the reasoning abilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient designs to mimic the habits and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor 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 releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and evaluate designs against key safety criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can [develop](https://gogs.dev.dazesoft.cn) multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 design, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:DorotheaRincon) improving user experiences and standardizing safety controls throughout your generative [AI](https://test.bsocial.buzz) 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 circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:AlexandraRaney9) validate you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limitation increase, create a limitation increase 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 proper AWS [Identity](https://www.ausfocus.net) and Gain Access To Management (IAM) permissions to use Amazon Bedrock [Guardrails](https://busanmkt.com). For directions, see Set up consents to use guardrails for material 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, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:MarcosAlbright7) avoid damaging content, and assess designs against essential safety requirements. You can implement security steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock [console](http://code.istudy.wang) or the API. For the example code to create the guardrail, see the GitHub repo.<br> |
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<br>The basic flow involves the following steps: First, the system gets an input for the model. This input is then processed through the [ApplyGuardrail API](http://luodev.cn). 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 result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples [showcased](https://www.sexmasters.xyz) in the following areas demonstrate 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 offers you access to over 100 popular, [surgiteams.com](https://surgiteams.com/index.php/User:TheodoreJenkin2) 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, select Model brochure 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 design. It doesn't support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.<br> |
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<br>The model detail page offers vital details about the design's capabilities, prices structure, and application guidelines. You can find detailed usage directions, [including sample](http://114.115.218.2309005) API calls and code snippets for integration. The model supports various text generation jobs, consisting of content production, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking capabilities. |
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The page also includes deployment options and licensing details to help you get going with DeepSeek-R1 in your applications. |
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3. To start utilizing DeepSeek-R1, select Deploy.<br> |
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<br>You will be prompted to set up the implementation 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 instances, enter a variety of instances (between 1-100). |
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6. For example 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 advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For a lot of use cases, the default [settings](https://git.muhammadfahri.com) will work well. However, for production deployments, you may desire to evaluate these settings to align with your company's security and compliance requirements. |
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7. Choose Deploy to start using 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 ground. |
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8. Choose Open in play ground to access an interactive interface where you can experiment with various prompts and change model criteria like temperature and optimum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, content for reasoning.<br> |
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<br>This is an exceptional way to explore the model's thinking and text generation abilities before integrating it into your applications. The play ground provides instant feedback, assisting you comprehend how the model reacts to various inputs and letting you tweak your triggers for optimal outcomes.<br> |
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<br>You can rapidly test the model in the play ground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br> |
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<br>Run inference utilizing guardrails with the [released](https://gitlab.digineers.nl) DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to [perform reasoning](https://cheapshared.com) utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends a request to create text based on a user prompt.<br> |
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<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br> |
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<br>SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses two hassle-free methods: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the technique that finest matches your requirements.<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, choose Studio in the [navigation pane](http://git.365zuoye.com). |
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2. First-time users will be triggered to develop 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 company name and model abilities.<br> |
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<br>4. Search for DeepSeek-R1 to view 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 name |
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- [Task classification](https://saathiyo.com) (for instance, Text Generation). |
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Bedrock Ready badge (if appropriate), indicating that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design<br> |
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<br>5. Choose the design card to view the design details page.<br> |
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<br>The design details page includes the following details:<br> |
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<br>- The model name and service provider details. |
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[Deploy button](https://collegestudentjobboard.com) to deploy the model. |
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About and [Notebooks tabs](http://101.33.225.953000) 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](http://artsm.net) [description](http://povoq.moe1145). |
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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 design, it's [recommended](http://163.66.95.1883001) to review the model details and license terms to confirm compatibility with your use case.<br> |
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<br>6. Choose Deploy to continue with release.<br> |
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<br>7. For Endpoint name, utilize the automatically produced name or develop a custom one. |
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8. For Instance type ¸ select a [circumstances](http://120.77.240.2159701) type (default: [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:NapoleonNeuman) ml.p5e.48 xlarge). |
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9. For Initial circumstances count, go into the number of circumstances (default: 1). |
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Selecting suitable instance types and counts is [essential](http://101.51.106.216) for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for [sustained traffic](https://gitlab.digineers.nl) and [low latency](https://www.speedrunwiki.com). |
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10. Review all setups for accuracy. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. |
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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 complete.<br> |
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<br>When deployment is complete, your endpoint status will change to InService. At this moment, the model is ready to accept reasoning requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release 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 begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning 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 [extra demands](http://git.zhiweisz.cn3000) against the predictor:<br> |
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<br>Implement guardrails and run inference 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 develop a guardrail utilizing the Amazon Bedrock [console](http://8.130.52.45) or the API, and [execute](http://copyvance.com) it as displayed in the following code:<br> |
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<br>Clean up<br> |
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<br>To avoid undesirable 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 released the design utilizing Amazon Bedrock Marketplace, complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations. |
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2. In the Managed implementations section, find the endpoint you wish to delete. |
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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](https://memorial-genweb.org) the proper implementation: 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 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 release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart [Foundation](https://e-sungwoo.co.kr) 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](https://namoshkar.com) business construct ingenious options utilizing AWS services and accelerated compute. Currently, he is concentrated on developing methods for fine-tuning and optimizing the inference efficiency of big language designs. In his downtime, Vivek delights in treking, watching films, and attempting different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://hub.tkgamestudios.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://richonline.club) [accelerators](http://123.207.52.1033000) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is an Expert Solutions Architect [dealing](http://jerl.zone3000) with generative [AI](https://spillbean.in.net) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial [intelligence](http://101.52.220.1708081) and [generative](http://www.fun-net.co.kr) [AI](https://39.98.119.14) hub. She is enthusiastic about constructing options that assist clients accelerate their [AI](https://gamberonmusic.com) journey and unlock service value.<br> |
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