Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart
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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 deploy DeepSeek [AI](https://www.hireprow.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](https://avicii.blog) concepts on AWS.<br>
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<br>In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow 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 large language model (LLM) developed by DeepSeek [AI](https://hcp.com.gt) that uses support finding out to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying feature is its reinforcement knowing (RL) step, which was used to improve the design's actions beyond the basic pre-training and fine-tuning procedure. By [integrating](https://scfr-ksa.com) RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both significance and [clearness](http://git.zltest.com.tw3333). In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's equipped to break down complicated queries and factor through them in a detailed way. This directed reasoning procedure enables the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:LynwoodBolling) aiming to create structured reactions while concentrating on interpretability and [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:GonzaloVue84412) user [interaction](http://ccconsult.cn3000). With its wide-ranging abilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, logical thinking and information interpretation jobs.<br>
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, making it possible for efficient inference by routing inquiries to the most appropriate expert "clusters." This method permits the design to focus on various problem domains while maintaining total [effectiveness](http://47.112.106.1469002). DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more effective architectures based on popular open [designs](https://myteacherspool.com) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a [process](https://git.rongxin.tech) of [training](https://psuconnect.in) smaller, more [effective designs](https://www.cbtfmytube.com) to mimic the habits and [reasoning patterns](https://sunriji.com) of the larger DeepSeek-R1 model, utilizing 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](https://gitea.imwangzhiyu.xyz) model, we recommend releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and evaluate designs against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://flixtube.org) applications.<br>
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<br>Prerequisites<br>
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<br>To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're utilizing 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 ask for a limit boost, create a limitation boost demand 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) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Establish approvals to use [guardrails](http://h.gemho.cn7099) 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, [surgiteams.com](https://surgiteams.com/index.php/User:JamieBingaman8) avoid hazardous material, and examine models against key security requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use [guardrails](https://pelangideco.com) to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create 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 circulation includes the following actions: First, the system receives an input for the design. This input is then processed through the [ApplyGuardrail API](https://git.russell.services). If the input passes the [guardrail](https://code.52abp.com) check, it's sent to the design for inference. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the last 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](http://code.qutaovip.com) 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 designs (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, select Model brochure under Foundation models in the navigation pane.
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At the time of composing this post, you can use the InvokeModel API to invoke the model. 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 provides important details about the design's capabilities, pricing structure, and execution standards. You can discover detailed usage guidelines, [including sample](http://hanbitoffice.com) API calls and code snippets for combination. The design supports numerous text generation jobs, consisting of material production, code generation, and question answering, utilizing its reinforcement learning 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 utilizing DeepSeek-R1, select Deploy.<br>
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<br>You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
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5. For Variety of instances, enter a variety of instances (between 1-100).
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6. For example type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
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Optionally, you can set up innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may wish to review these settings to line up 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 release is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
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8. Choose Open in play area to access an interactive interface where you can try out different prompts and adjust model parameters like temperature level and optimum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, material for reasoning.<br>
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<br>This is an exceptional way to explore the design's thinking and text generation capabilities before incorporating it into your applications. The play area offers instant feedback, helping you understand how the model reacts to various inputs and letting you tweak your triggers for ideal results.<br>
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<br>You can [rapidly evaluate](https://www.jobs.prynext.com) the model in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
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<br>Run reasoning 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 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](https://vybz.live) the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends out a request to generate text based upon 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 release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1075260) deploy them into production utilizing either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you pick the approach that best [matches](https://bertlierecruitment.co.za) your needs.<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, pick 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, [pick JumpStart](http://47.108.239.2023001) in the navigation pane.<br>
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<br>The model browser displays available designs, with details like the company name and design abilities.<br>
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 [design card](http://quickad.0ok0.com).
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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 instance, Text Generation).
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[Bedrock Ready](http://git.agdatatec.com) badge (if suitable), showing that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to [conjure](https://fydate.com) up the design<br>
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<br>5. Choose the model card to see 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 supplier details.
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Deploy button to deploy the model.
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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 description.
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- License details.
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- Technical requirements.
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- Usage standards<br>
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<br>Before you deploy the model, 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, utilize the immediately [produced](https://www.cbl.aero) name or create a custom-made one.
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8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
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9. For count, enter the number of circumstances (default: 1).
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Selecting suitable instance types and counts is important for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, [Real-time inference](http://39.106.177.1608756) is selected by default. This is optimized for sustained traffic and low latency.
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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 seclusion remains in place.
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11. Choose Deploy to release the model.<br>
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<br>The implementation process can take a number of minutes to finish.<br>
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<br>When implementation is complete, your endpoint status will alter to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the model 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 going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and range 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce 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>Clean up<br>
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<br>To avoid undesirable charges, complete the actions in this area to tidy up your resources.<br>
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<br>Delete the [Amazon Bedrock](http://34.236.28.152) Marketplace implementation<br>
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<br>If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:<br>
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations.
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2. In the Managed implementations area, locate the endpoint 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 correct implementation: 1. Endpoint name.
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2. Model name.
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3. Endpoint status<br>
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<br>Delete the SageMaker JumpStart predictor<br>
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<br>The SageMaker JumpStart model you deployed will sustain costs if you leave it [running](http://git.youkehulian.cn). Use the following code to erase the endpoint if you wish 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](https://login.discomfort.kz) pretrained designs, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:RosieG65174) 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 helps emerging generative [AI](https://vieclam.tuoitrethaibinh.vn) companies construct innovative options utilizing [AWS services](https://express-work.com) and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the reasoning efficiency of large language models. In his complimentary time, Vivek enjoys treking, enjoying films, and trying various cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://v-jobs.net) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://git.buzhishi.com:14433) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
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<br>Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://diskret-mote-nodeland.jimmyb.nl) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://git.ai-robotics.cn) center. She is passionate about constructing solutions that help clients accelerate their [AI](http://durfee.mycrestron.com:3000) journey and unlock business worth.<br>
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