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 excited to announce that DeepSeek R1 [distilled Llama](https://www.philthejob.nl) and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:QYKElton1324495) you can now release DeepSeek [AI](https://tageeapp.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your [generative](https://www.webthemes.ca) [AI](http://101.200.181.61) ideas on AWS.<br>
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<br>In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models also.<br>
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<br>Overview of DeepSeek-R1<br>
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<br>DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](http://h.gemho.cn:7099) that utilizes reinforcement finding out to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying function is its support knowing (RL) action, which was used to improve the design's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 [employs](https://gitea.qianking.xyz3443) a chain-of-thought (CoT) technique, indicating it's geared up to break down intricate questions and factor through them in a detailed way. This directed thinking process allows the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be integrated into different workflows such as representatives, logical reasoning and data analysis jobs.<br>
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion [specifications](https://findschools.worldofdentistry.org) in size. The MoE architecture enables activation of 37 billion specifications, making it possible for efficient reasoning by routing queries to the most relevant specialist "clusters." This technique permits the design to specialize in various problem domains while maintaining total performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more 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 sized, more effective models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher model.<br>
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<br>You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1321201) and examine models against crucial safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 design, [enhancing](https://evertonfcfansclub.com) user experiences and standardizing security controls across your generative [AI](http://szelidmotorosok.hu) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To [inspect](http://121.28.134.382039) if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limit increase, develop a limit boost 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 correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Set up 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 material, and examine designs against [essential security](https://gitlab.wah.ph) criteria. You can carry out safety procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using 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 general flow includes the following steps: [wiki-tb-service.com](http://wiki-tb-service.com/index.php?title=Benutzer:TobiasChristison) First, the system receives an input for the model. This input is then processed through the [ApplyGuardrail API](http://greenmk.co.kr). If the input passes the guardrail check, it's sent to the model for reasoning. After getting the model's output, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:CarlTabarez70) another guardrail check is used. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in 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 [demonstrate reasoning](https://usa.life) using this API.<br>
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
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<br>Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:ChetHeller473) and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:<br>
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<br>1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane.
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At the time of writing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.<br>
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<br>The model detail page supplies vital details about the design's abilities, pricing structure, and execution standards. You can discover detailed usage directions, consisting of sample API calls and code bits for integration. The model supports numerous text generation tasks, consisting of content development, code generation, and concern answering, using its support learning optimization and CoT reasoning capabilities.
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The page also includes release choices and licensing details to assist you begin with DeepSeek-R1 in your applications.
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3. To begin using DeepSeek-R1, choose Deploy.<br>
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<br>You will be prompted to set up the release details for DeepSeek-R1. The design 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, choose your instance type. For optimum efficiency with DeepSeek-R1, a [GPU-based circumstances](http://210.236.40.2409080) type like ml.p5e.48 xlarge is advised.
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Optionally, you can configure advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, [service function](https://insta.kptain.com) consents, and file encryption settings. For many use cases, the default settings will work well. However, for production deployments, you might wish to examine these settings to align with your company'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 total, you can check 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 user interface where you can try out different triggers and change design criteria like temperature level and maximum length.
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, content for inference.<br>
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<br>This is an excellent way to check out the design's thinking and text generation capabilities before incorporating it into your applications. The play ground offers immediate feedback, helping you comprehend how the model reacts to various inputs and letting you fine-tune your prompts for optimal results.<br>
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<br>You can rapidly evaluate the design in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
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<br>Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually [developed](http://hrplus.com.vn) the guardrail, use the following code to implement guardrails. The script [initializes](https://www.graysontalent.com) the bedrock_runtime client, configures 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) hub with FMs, built-in algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, [it-viking.ch](http://it-viking.ch/index.php/User:MonikaTempleton) 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 convenient techniques: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the [SageMaker Python](http://secdc.org.cn) SDK. Let's check out both [methods](https://www.kukustream.com) to help you choose the method that best matches your needs.<br>
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
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<br>Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:<br>
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<br>1. On the SageMaker console, pick Studio in the navigation pane.
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2. First-time users will be triggered to 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 model internet browser displays available designs, with details like the service provider name and design capabilities.<br>
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
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Each design card shows key details, including:<br>
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<br>- Model name
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- Provider name
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- Task classification (for [gratisafhalen.be](https://gratisafhalen.be/author/willianl17/) example, Text Generation).
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Bedrock Ready badge (if appropriate), suggesting that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon [Bedrock](http://git.armrus.org) APIs to conjure up the model<br>
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<br>5. Choose the model card to see the model details page.<br>
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<br>The design details page includes the following details:<br>
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<br>- The design name and company details.
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Deploy button to release the model.
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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 standards<br>
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<br>Before you deploy the model, it's suggested to examine the model details and license terms to validate compatibility with your usage case.<br>
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<br>6. Choose Deploy to proceed with deployment.<br>
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<br>7. For Endpoint name, utilize the instantly created name or [produce](https://intgez.com) a customized one.
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8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
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9. For Initial instance count, go into the variety of circumstances (default: 1).
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Selecting appropriate circumstances types and counts is important for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and [low latency](https://www.cvgods.com).
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10. Review all configurations for precision. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
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11. Choose Deploy to deploy the design.<br>
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<br>The implementation procedure can take a number of minutes to finish.<br>
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<br>When implementation is total, your endpoint status will change to InService. At this point, the model is prepared to accept reasoning requests through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can conjure up the design using a SageMaker 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 started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need 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 deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and range from [SageMaker Studio](https://gitea.mpc-web.jp).<br>
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<br>You can run additional requests 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 create a guardrail utilizing the Amazon Bedrock console or the API, and [implement](https://git.coalitionofinvisiblecolleges.org) it as displayed in the following code:<br>
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<br>Clean up<br>
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<br>To avoid [undesirable](https://jobstoapply.com) charges, complete the actions in this area to tidy 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 models in the navigation pane, pick Marketplace deployments.
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2. In the Managed implementations section, locate the endpoint you desire to delete.
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3. Select the endpoint, and on the Actions menu, [select Delete](https://gitlab.interjinn.com).
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4. Verify the endpoint details to make certain you're erasing 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 design you deployed 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 release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit in SageMaker Studio or Amazon Bedrock Marketplace 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 Beginning 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://app.zamow-kontener.pl) business construct ingenious solutions using AWS services and accelerated compute. Currently, he is concentrated on developing methods for [fine-tuning](https://jandlfabricating.com) and optimizing the inference performance of big language designs. In his leisure time, Vivek enjoys hiking, seeing motion pictures, and attempting different cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://paksarkarijob.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://gitea.infomagus.hu) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
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<br>[Jonathan Evans](https://gitea.sync-web.jp) is a Professional Solutions Architect dealing with generative [AI](http://stay22.kr) with the Third-Party Model Science group 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://astonvillafansclub.com) center. She is enthusiastic about developing services that help customers accelerate their [AI](https://www.hrdemployment.com) journey and [unlock organization](https://www.naukrinfo.pk) value.<br>
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