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 delighted to announce that [DeepSeek](https://www.footballclubfans.com) R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://cruzazulfansclub.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion [criteria](https://musicplayer.hu) to develop, experiment, and properly scale your generative [AI](https://wiki.team-glisto.com) ideas on AWS.<br>
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<br>In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the models too.<br>
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<br>Overview of DeepSeek-R1<br>
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<br>DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://gitee.mmote.ru) that uses reinforcement discovering to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying function is its support knowing (RL) step, which was utilized to improve the [design's reactions](https://classificados.diariodovale.com.br) beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually improving both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, implying it's geared up to break down complicated queries and reason through them in a detailed manner. This guided thinking process enables the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while [focusing](https://gitea.ochoaprojects.com) on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, logical reasoning and data 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 enables activation of 37 billion parameters, allowing efficient reasoning by routing inquiries to the most relevant professional "clusters." This [approach](https://play.future.al) enables the model to specialize in different issue domains while maintaining total effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of [GPU memory](http://175.178.153.226).<br>
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<br>DeepSeek-R1 distilled models bring the [thinking](http://1.12.246.183000) [capabilities](https://www.drawlfest.com) of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective models to [imitate](http://116.62.115.843000) the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher design.<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, [wavedream.wiki](https://wavedream.wiki/index.php/User:KarlBeardsley7) we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and examine models against key safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](http://www.thehispanicamerican.com) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 design, you require access to an ml.p5e [circumstances](https://bestwork.id). To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify 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 releasing. To request a limit increase, produce a limitation increase demand [oeclub.org](https://oeclub.org/index.php/User:MauricioRdz) and connect to your account team.<br>
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<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Establish approvals 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 introduce safeguards, avoid harmful content, and examine designs against key safety criteria. You can execute security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and design 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 flow involves the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections 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 gives you access to over 100 popular, emerging, and [specialized structure](http://47.122.26.543000) models (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 designs in the navigation pane.
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At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.<br>
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<br>The design detail page offers important details about the design's capabilities, rates structure, and application guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for integration. The model supports different text generation tasks, [including](http://120.77.240.2159701) content production, code generation, and question answering, using its reinforcement discovering optimization and CoT reasoning capabilities.
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The page also includes deployment alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications.
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3. To start using DeepSeek-R1, choose Deploy.<br>
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<br>You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated.
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4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
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5. For Variety of circumstances, go into a number of instances (between 1-100).
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6. For example type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
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Optionally, you can set up sophisticated security and facilities settings, consisting of virtual personal 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 deployments, you may wish to examine these settings to line up with your company's security and compliance requirements.
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7. Choose Deploy to start utilizing the design.<br>
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<br>When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
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8. Choose Open in playground to access an interactive user interface where you can experiment with various triggers and change model specifications like temperature and optimum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, material for reasoning.<br>
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<br>This is an excellent method to explore the design's reasoning and [wiki.whenparked.com](https://wiki.whenparked.com/User:JZKMireya164733) text [generation abilities](https://git.perrocarril.com) before incorporating it into your applications. The play area offers immediate feedback, assisting you understand how the design reacts to different inputs and letting you tweak your triggers for ideal results.<br>
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<br>You can rapidly test the model in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
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<br>Run reasoning using [guardrails](https://premiergitea.online3000) with the [deployed](https://janhelp.co.in) DeepSeek-R1 endpoint<br>
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<br>The following code example demonstrates how to perform reasoning using a [released](https://fydate.com) DeepSeek-R1 design through Amazon Bedrock utilizing 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 produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures 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) center with FMs, integrated algorithms, and prebuilt ML services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://maibuzz.com) designs to your use case, with your information, and deploy them into production using either the UI or SDK.<br>
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<br>[Deploying](https://mp3talpykla.com) DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the method that finest 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](https://dandaelitetransportllc.com) 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 service provider name and design capabilities.<br>
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<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
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Each model card reveals key details, including:<br>
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<br>- Model name
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- Provider name
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- Task category (for example, Text Generation).
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Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke 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 model details page consists of the following details:<br>
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<br>- The design name and company details.
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Deploy button to release the design.
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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](http://101.132.73.143000) 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 advised to review the [design details](https://macphersonwiki.mywikis.wiki) and license terms to confirm 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, use the [automatically generated](https://gitlabdemo.zhongliangong.com) name or produce a customized one.
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8. For example [type ¸](https://sb.mangird.com) choose a circumstances 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 proper](http://47.103.91.16050903) instance types and counts is essential for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
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10. Review all configurations for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
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11. Choose Deploy to release the design.<br>
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<br>The deployment procedure can take numerous minutes to complete.<br>
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<br>When release is total, your endpoint status will change to InService. At this point, the model is ready to accept reasoning demands through the endpoint. You can keep track of the implementation progress on the [SageMaker](https://git.whistledev.com) [console Endpoints](https://www.passadforbundet.se) page, which will display pertinent metrics and status details. When the release is total, you can [conjure](https://talentsplendor.com) up the design utilizing a [SageMaker](https://cheere.org) runtime customer and integrate it with your applications.<br>
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<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
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<br>To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:PoppyForand) run from SageMaker Studio.<br>
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<br>You can run extra requests 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement 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 area to clean 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 model using Amazon Bedrock Marketplace, complete the following actions:<br>
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments.
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2. In the Managed releases section, find the [endpoint](https://www.jobs-f.com) you desire 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 deleting the right release: 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. 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 checked out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://owow.chat) business construct ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the [reasoning performance](https://git.creeperrush.fun) of big language models. In his downtime, Vivek enjoys hiking, watching motion pictures, and attempting different cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://zeus.thrace-lan.info:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://47.104.60.158:7777) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
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<br>Jonathan Evans is a Specialist Solutions Architect working on [generative](http://tv.houseslands.com) [AI](https://git.hxps.ru) 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 [AI](https://lidoo.com.br) hub. She is enthusiastic about developing services that help clients accelerate their [AI](https://superappsocial.com) journey and unlock organization value.<br>
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