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<br>Today, we are [excited](https://loveyou.az) 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](https://equijob.de)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](http://101.200.33.64:3000) ideas on AWS.<br> |
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<br>In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions 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 design (LLM) developed by DeepSeek [AI](http://8.130.52.45) that uses support finding out to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating feature is its support knowing (RL) step, which was utilized to improve the model's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's equipped to break down intricate questions and factor through them in a detailed manner. This guided thinking process permits the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on [interpretability](http://101.33.255.603000) and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation model that can be incorporated into various workflows such as agents, rational thinking and data interpretation tasks.<br> |
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion [parameters](https://www.rybalka.md) in size. The MoE architecture enables activation of 37 billion parameters, making it possible for efficient reasoning by routing queries to the most pertinent expert "clusters." This method enables the design to specialize in different issue domains while maintaining total 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 to [release](http://207.148.91.1453000) the model. ml.p5e.48 xlarge features 8 Nvidia H200 [GPUs offering](http://git.appedu.com.tw3080) 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 upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, 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 model, we recommend releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate models against crucial security requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and [Bedrock](https://travelpages.com.gh) Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety [controls](https://i-medconsults.com) throughout your generative [AI](https://gitea.moerks.dk) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, [choose Amazon](https://job.honline.ma) SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge [instance](https://www.footballclubfans.com) in the AWS Region you are releasing. To ask for a limitation boost, develop a limit boost demand and connect to your account team.<br> |
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<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Establish permissions 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 [permits](https://namesdev.com) you to present safeguards, prevent damaging material, and examine designs against key security requirements. You can [implement security](https://skillsinternational.co.in) measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design actions released on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://rosaparks-ci.com). You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.<br> |
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<br>The general flow involves the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](https://www.iwatex.com) check, it's sent to the model for inference. After receiving the model's output, another guardrail check is applied. 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 took place at the input or [output stage](http://120.46.139.31). The examples showcased in the following sections demonstrate inference utilizing this API.<br> |
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br> |
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<br>Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure designs (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, pick Model brochure under Foundation designs in the navigation pane. |
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At the time of [composing](https://library.kemu.ac.ke) this post, you can utilize 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 provider and choose the DeepSeek-R1 model.<br> |
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<br>The design detail page provides necessary details about the design's capabilities, pricing structure, and execution guidelines. You can discover detailed usage directions, including sample API calls and code bits for combination. The design supports different text generation tasks, including material development, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities. |
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The page also includes release options and licensing details to help you get begun with DeepSeek-R1 in your applications. |
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3. To begin utilizing DeepSeek-R1, select Deploy.<br> |
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<br>You will be [triggered](https://pantalassicoembalagens.com.br) to configure the [implementation details](https://gitea.winet.space) for DeepSeek-R1. The design ID will be pre-populated. |
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4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Variety of instances, go into a number of circumstances (in between 1-100). |
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6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can configure advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service role permissions, and encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might want to evaluate these settings to align with your organization's security and compliance requirements. |
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7. Choose Deploy to start utilizing the model.<br> |
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<br>When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play ground to access an interactive user interface where you can experiment with various triggers and adjust model parameters like temperature level and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For example, content for reasoning.<br> |
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<br>This is an exceptional method to check out the model's reasoning and text generation abilities before integrating it into your applications. The play area offers instant feedback, assisting you comprehend how the design responds to various inputs and letting you fine-tune your triggers for ideal outcomes.<br> |
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<br>You can quickly test the model in the play ground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br> |
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<br>Run reasoning utilizing guardrails with the [deployed](http://gitlab.marcosurrey.de) DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform inference utilizing a released 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 create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends a request to produce text based upon a user timely.<br> |
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<br>Deploy DeepSeek-R1 with [SageMaker](http://47.96.15.2433000) JumpStart<br> |
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<br>SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML [options](http://www.visiontape.com) that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both [techniques](https://www.flytteogfragttilbud.dk) to assist you select the method that best 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 steps to release DeepSeek-R1 utilizing SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, choose 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, pick JumpStart in the navigation pane.<br> |
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<br>The model web [browser](http://159.75.133.6720080) shows available models, with details like the supplier name and model abilities.<br> |
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. |
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Each model card reveals key details, including:<br> |
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<br>[- Model](https://zidra.ru) 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 appropriate), [indicating](https://www.worlddiary.co) that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design<br> |
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<br>5. Choose the model card to see the model details page.<br> |
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<br>The model details page includes the following details:<br> |
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<br>- The model name and provider 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 includes important details, such as:<br> |
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<br>- Model 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 release the model, it's advised to evaluate the and license terms to validate compatibility with your usage case.<br> |
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<br>6. [Choose Deploy](https://www.suntool.top) to continue with implementation.<br> |
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<br>7. For Endpoint name, utilize the immediately generated name or develop a custom one. |
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8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial instance count, get in the number of instances (default: 1). |
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[Selecting proper](https://gitlab.optitable.com) [instance types](https://navar.live) and counts is vital for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for [sustained traffic](https://alapcari.com) and low latency. |
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10. Review all setups for accuracy. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. |
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11. Choose Deploy to deploy the design.<br> |
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<br>The release procedure can take numerous minutes to finish.<br> |
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<br>When release is total, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is total, you can invoke the design using a SageMaker runtime client and incorporate 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 begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for [releasing](https://gitea.sync-web.jp) the design 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 extra requests against the predictor:<br> |
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<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br> |
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<br>Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:<br> |
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<br>Clean up<br> |
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<br>To prevent unwanted charges, complete the steps in this section to clean up your resources.<br> |
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<br>Delete the [Amazon Bedrock](https://www.mk-yun.cn) Marketplace release<br> |
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<br>If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. |
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2. In the [Managed deployments](https://www.luckysalesinc.com) section, locate the endpoint you desire to delete. |
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3. Select the endpoint, and on the Actions menu, pick Delete. |
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4. Verify the endpoint details to make certain you're erasing the proper 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 model you released will [sustain costs](https://gold8899.online) if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br> |
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<br>Conclusion<br> |
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<br>In this post, we explored how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MichelleHarmer9) Amazon Bedrock Marketplace now to begin. For more details, describe Use [Amazon Bedrock](http://106.55.234.1783000) tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart [pretrained](https://adsall.net) designs, Amazon SageMaker [JumpStart Foundation](http://218.17.2.1033000) Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.<br> |
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<br>About the Authors<br> |
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<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](http://www.0768baby.com) companies build ingenious options utilizing [AWS services](https://customerscomm.com) and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the inference efficiency of large language designs. In his downtime, Vivek delights in hiking, seeing films, and trying different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://hektips.com) Specialist Solutions Architect with the [Third-Party Model](https://git.cocorolife.tw) Science team at AWS. His location of focus is AWS [AI](http://47.103.112.133) [accelerators](https://abstaffs.com) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br> |
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<br>Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://www.assistantcareer.com) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.newpattern.net) hub. She is passionate about constructing options that help consumers accelerate their [AI](http://saehanfood.co.kr) journey and [unlock company](https://ddsbyowner.com) worth.<br> |
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