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<br>Today, we are thrilled to reveal that DeepSeek 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](http://47.119.160.181:3000)'s first-generation [frontier](https://scm.fornaxian.tech) design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://xinh.pro.vn) concepts 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 [yewiki.org](https://www.yewiki.org/User:MayaGinn22) SageMaker JumpStart. You can follow similar 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://62.178.96.192:3000) that uses reinforcement learning to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A [key distinguishing](https://git.cooqie.ch) feature is its reinforcement knowing (RL) action, which was used to improve the design's reactions beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately enhancing both significance and [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:WileyK1034) clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down [complicated questions](https://video.invirtua.com) and factor through them in a detailed way. This assisted reasoning process allows the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, logical reasoning and data analysis tasks.<br> |
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, enabling effective inference by routing queries to the most relevant professional "clusters." This approach permits the model to specialize in various problem domains while maintaining overall effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more efficient architectures based on 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, more effective designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.<br> |
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<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or . Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and assess designs against crucial security requirements. At the time of composing this blog site, for DeepSeek-R1 releases on [SageMaker JumpStart](https://firstcanadajobs.ca) and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://scm.fornaxian.tech) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, [links.gtanet.com.br](https://links.gtanet.com.br/terilenz4996) and verify you're using 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, produce a [limit increase](https://easterntalent.eu) request and connect to your account team.<br> |
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<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS [Identity](https://drshirvany.ir) and Gain Access To Management (IAM) authorizations to utilize Amazon [Bedrock Guardrails](http://skupra-nat.uamt.feec.vutbr.cz30000). For instructions, see Set up approvals to utilize guardrails for content filtering.<br> |
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<br>Implementing guardrails with the ApplyGuardrail API<br> |
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<br>Amazon Bedrock Guardrails enables you to introduce safeguards, avoid [hazardous](https://git.bubblesthebunny.com) material, and assess models against crucial safety requirements. You can implement security steps for the DeepSeek-R1 design utilizing the [Amazon Bedrock](https://lidoo.com.br) ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.<br> |
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<br>The basic flow includes the following steps: First, the system [receives](https://git.komp.family) 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 model for inference. After receiving the model's output, another guardrail check is applied. 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 indicating the nature of the intervention and whether it took place at the input or output stage. 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 offers you access to over 100 popular, emerging, and specialized foundation models (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, pick Model catalog under Foundation models in the navigation pane. |
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At the time of [composing](https://gitlab.liangzhicn.com) this post, you can [utilize](https://elmerbits.com) the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.<br> |
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<br>The model detail page offers vital details about the design's capabilities, pricing structure, and execution guidelines. You can find detailed usage directions, including sample API calls and code snippets for integration. The design supports numerous text generation tasks, consisting of material development, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking capabilities. |
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The page likewise consists of deployment options and licensing details to help you get going 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 prompted to set up the deployment details for DeepSeek-R1. The model 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 Variety of circumstances, enter a variety of circumstances (in between 1-100). |
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6. For Instance type, select your instance type. For optimum efficiency with DeepSeek-R1, a [GPU-based instance](https://git.bwnetwork.us) type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can configure advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service role approvals, and [gratisafhalen.be](https://gratisafhalen.be/author/danarawson/) file encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might want to examine these settings to line up 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 implementation is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. |
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8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and change 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 template for optimal outcomes. For instance, material for reasoning.<br> |
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<br>This is an excellent way to explore the design's reasoning and text generation capabilities before integrating it into your [applications](https://jobs.campus-party.org). The play ground offers immediate feedback, assisting you understand how the model responds to different inputs and letting you tweak your triggers for optimal outcomes.<br> |
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<br>You can quickly check the model in the play area 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 inference utilizing guardrails with the deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to perform inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends a request to produce 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, built-in algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into [production](https://jobs.ethio-academy.com) using either the UI or [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:CHOEnid1821) SDK.<br> |
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient approaches: utilizing the user-friendly SageMaker [JumpStart](http://47.97.178.182) UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the approach that finest suits 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 release DeepSeek-R1 utilizing SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, select Studio in the navigation pane. |
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2. First-time users will be prompted to create 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 shows available models, 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 reveals essential details, consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- Task classification (for example, Text Generation). |
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Bedrock Ready badge (if applicable), [suggesting](https://canadasimple.com) that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model<br> |
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<br>5. Choose the design card to see the design details page.<br> |
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<br>The design details page consists of the following details:<br> |
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<br>- The model name and supplier details. |
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Deploy button to deploy the design. |
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About and Notebooks tabs with detailed details<br> |
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<br>The About tab consists of important details, such as:<br> |
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<br>- Model description. |
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- License details. |
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- Technical specs. |
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[- Usage](https://www.yanyikele.com) guidelines<br> |
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<br>Before you deploy the design, it's advised to evaluate the model details and license terms to verify compatibility with your use case.<br> |
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<br>6. Choose Deploy to continue with implementation.<br> |
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<br>7. For Endpoint name, use the instantly produced name or produce a custom one. |
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8. For Instance type [¸ choose](http://175.24.174.1733000) a circumstances type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, enter the number of circumstances (default: 1). |
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[Selecting suitable](https://119.29.170.147) circumstances types and counts is essential for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen 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 strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion 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 deployment is total, your endpoint status will change to InService. At this point, the model is prepared to accept inference requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can conjure up the model utilizing 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 utilizing the SageMaker Python SDK, you will require 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 release and use DeepSeek-R1 for inference programmatically. The code for [deploying](https://git.gilgoldman.com) 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 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](https://stagingsk.getitupamerica.com) JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as revealed in the following code:<br> |
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<br>Tidy 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 design utilizing Amazon Bedrock Marketplace, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the [navigation](https://jobs.campus-party.org) pane, pick Marketplace implementations. |
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2. In the [Managed releases](http://123.56.247.1933000) section, locate the [endpoint](https://social.oneworldonesai.com) 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 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 design you released will sustain costs if you leave it running. Use the following code to delete 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 and SageMaker JumpStart. Visit SageMaker [JumpStart](https://rosaparks-ci.com) in SageMaker Studio or Amazon Bedrock [Marketplace](https://upmasty.com) now to start. 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 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](https://www.youtoonetwork.com) Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://xotube.com) companies develop [innovative solutions](http://gs1media.oliot.org) using AWS services and sped up calculate. Currently, he is focused on developing techniques for fine-tuning and [enhancing](http://106.52.126.963000) the reasoning efficiency of big language designs. In his totally free time, Vivek takes pleasure in treking, enjoying motion pictures, and attempting different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://vibefor.fun) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](https://manilall.com) of focus is AWS [AI](https://4stour.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 Specialist Solutions Architect dealing with generative [AI](https://www.empireofember.com) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitea.nongnghiepso.com) center. She is passionate about developing solutions that assist customers accelerate their [AI](https://nse.ai) journey and [unlock organization](https://sublimejobs.co.za) worth.<br> |
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