Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are [thrilled](https://jobs.quvah.com) to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://git.e365-cloud.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative [AI](http://110.41.143.128:8081) concepts on AWS.<br> |
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<br>In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://es-africa.com). You can follow similar steps to deploy the distilled versions of the designs 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](http://www.chinajobbox.com) by DeepSeek [AI](http://jejuanimalnow.org) that utilizes support discovering to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying feature is its [reinforcement learning](https://lovn1world.com) (RL) step, which was utilized to refine the model's reactions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's equipped to break down complicated questions and factor [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) through them in a detailed way. This assisted thinking process allows the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while [concentrating](https://git.iws.uni-stuttgart.de) on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, sensible reasoning and data interpretation jobs.<br> |
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, making it possible for effective inference by routing questions to the most relevant specialist "clusters." This approach allows the model to concentrate on different issue domains while maintaining general efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 [xlarge circumstances](http://163.66.95.1883001) to deploy the design. 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 designs bring the reasoning capabilities of the main R1 model to more effective architectures based upon [popular](http://hmind.kr) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor design.<br> |
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<br>You can [release](http://119.3.70.2075690) DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and examine designs against key security criteria. 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 develop numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://nse.ai) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy 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](http://175.178.71.893000) and under AWS Services, select Amazon SageMaker, and validate you're using 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 limit boost, develop a limitation increase demand and connect to your account team.<br> |
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to use guardrails for content filtering.<br> |
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<br>Implementing guardrails with the ApplyGuardrail API<br> |
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<br>Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous material, and evaluate designs against crucial safety requirements. You can carry out security steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This [permits](http://124.71.134.1463000) you to apply guardrails to evaluate user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock [console](http://www.boot-gebraucht.de) or the API. For the example code to develop the guardrail, see the GitHub repo.<br> |
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<br>The basic flow includes the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for inference. After receiving the model'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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas show 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 gives 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, select Model catalog 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](https://git.xaviermaso.com) tooling. |
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2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.<br> |
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<br>The model detail page supplies important details about the model's abilities, prices structure, and implementation guidelines. You can find detailed use directions, including sample API calls and code snippets for combination. The design supports numerous [text generation](https://gitea.linkensphere.com) jobs, consisting of content development, code generation, and concern answering, using its support discovering optimization and CoT reasoning capabilities. |
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The page likewise includes release alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. |
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3. To start using DeepSeek-R1, pick 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 Number of instances, enter a variety of instances (in between 1-100). |
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6. For example type, choose your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can configure innovative security and facilities settings, including virtual private cloud (VPC) networking, service role approvals, and encryption settings. For many use cases, the default settings will work well. However, for production releases, you might wish to examine these settings to align with your organization's security and compliance requirements. |
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7. Choose Deploy to begin using the model.<br> |
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<br>When the deployment is complete, you can check 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 explore different prompts and adjust model specifications like temperature level and optimum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, material for reasoning.<br> |
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<br>This is an excellent method to explore the design's thinking and text generation abilities before incorporating it into your [applications](https://bpx.world). The playground provides instant feedback, assisting you understand how the model reacts to numerous inputs and letting you fine-tune your prompts for ideal 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 design 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 DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to carry out reasoning using 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 created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, and sends a demand to generate 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) hub with FMs, built-in algorithms, and prebuilt ML services that you can [release](http://40.73.118.158) with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into [production utilizing](https://gochacho.com) either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free approaches: using the user-friendly SageMaker JumpStart UI or out programmatically through the SageMaker Python SDK. Let's [explore](https://social.sktorrent.eu) both approaches to help you choose the approach that finest fits your requirements.<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 [prompted](https://8.129.209.127) to create a domain. |
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3. On the SageMaker Studio console, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11925076) choose JumpStart in the navigation pane.<br> |
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<br>The model browser shows available designs, with details like the service provider name and design abilities.<br> |
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<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. |
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Each design card shows crucial details, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:SerenaM745) consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- Task classification (for instance, Text Generation). |
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Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, enabling you to use [Amazon Bedrock](https://git.phyllo.me) APIs to invoke the model<br> |
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<br>5. Choose the design card to see the model details page.<br> |
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<br>The model details page consists of the following details:<br> |
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<br>- The design name and service provider details. |
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[Deploy button](https://yourmoove.in) 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 crucial details, such as:<br> |
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<br>[- Model](https://nojoom.net) description. |
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- License details. |
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- Technical specifications. |
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- Usage standards<br> |
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<br>Before you deploy the design, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:RachelSantos0) it's recommended to evaluate the model details and license terms to validate compatibility with your usage 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-made one. |
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8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, get in the variety of circumstances (default: 1). |
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Selecting suitable instance types and counts is essential for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference 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 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 model.<br> |
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<br>The deployment process can take numerous minutes to finish.<br> |
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<br>When release is total, your endpoint status will alter to [InService](https://viraltry.com). At this point, the design is ready to accept inference requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime client 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 [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2768920) make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.<br> |
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<br>You can run additional 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) implement it as shown in the following code:<br> |
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<br>Tidy up<br> |
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<br>To avoid [undesirable](http://47.110.52.1323000) charges, finish the actions in this area to clean up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace implementation<br> |
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<br>If you deployed the model using [Amazon Bedrock](https://travelpages.com.gh) Marketplace, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. |
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2. In the Managed deployments area, locate the endpoint you want to erase. |
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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 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 design 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 checked out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon [SageMaker JumpStart](https://git.fhlz.top).<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://kohentv.flixsterz.com) business develop innovative options using AWS services and sped up calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the inference efficiency of big [language designs](https://tjoobloom.com). In his downtime, Vivek enjoys hiking, viewing motion pictures, and attempting different foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://galmudugjobs.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://blazblue.wiki) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [Science](http://repo.jd-mall.cn8048) and Bioinformatics.<br> |
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<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://www.visiontape.com) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://comunidadebrasilbr.com) center. She is enthusiastic about developing solutions that assist clients accelerate their [AI](https://lgmtech.co.uk) journey and unlock business worth.<br> |
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