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<br>Today, we are [excited](https://www.social.united-tuesday.org) to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://git.taokeapp.net:3000)'s design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://tenacrebooks.com) 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 comparable actions to deploy the distilled versions of the models 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](https://tyciis.com) by DeepSeek [AI](https://music.elpaso.world) that utilizes reinforcement [finding](https://sahabatcasn.com) out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its support learning (RL) action, which was utilized to improve the model's actions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, [ultimately improving](https://unitenplay.ca) both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's geared up to break down complicated inquiries and reason through them in a detailed way. This directed thinking procedure permits the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to create [structured actions](http://47.98.190.109) while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, logical thinking and information interpretation tasks.<br> |
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, enabling effective [inference](https://app.hireon.cc) by routing questions to the most relevant specialist "clusters." This technique allows the model to concentrate on various issue domains while maintaining general performance. DeepSeek-R1 requires at least 800 GB of [HBM memory](http://207.180.250.1143000) in FP8 format for inference. In this post, we will use an ml.p5e.48 [xlarge instance](http://154.64.253.773000) to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of [GPU memory](https://zapinacz.pl).<br> |
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<br>DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model 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 procedure of training smaller, more efficient models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor 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 model with guardrails in [location](http://www.vpsguards.co). In this blog, we will use Amazon Bedrock Guardrails to present safeguards, [prevent hazardous](https://vmi456467.contaboserver.net) content, and assess designs against crucial safety criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](https://twoo.tr) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're using 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 limitation increase, develop a limitation increase request and reach out to your [account team](http://111.229.9.193000).<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 use 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 enables you to present safeguards, prevent hazardous content, and assess designs against key safety criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and [model responses](http://39.101.179.1066440) released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the [GitHub repo](https://sugarmummyarab.com).<br> |
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<br>The general flow includes the following actions: First, [wavedream.wiki](https://wavedream.wiki/index.php/User:TammieRaposo6) the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. 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](http://digitalmaine.net) at the input or output phase. The examples showcased in the following areas show 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 provides 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 writing this post, you can utilize the [InvokeModel API](https://www.mafiscotek.com) to conjure up the model. It doesn't [support Converse](https://cambohub.com3000) APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.<br> |
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<br>The design detail page provides essential details about the model's capabilities, prices structure, and application guidelines. You can find detailed usage guidelines, consisting of sample API calls and code snippets for combination. The model supports numerous text generation tasks, including material creation, code generation, and concern answering, using its support learning optimization and CoT thinking capabilities. |
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The page also includes deployment alternatives and licensing details to help you get going 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 configure the [implementation details](https://career.webhelp.pk) for DeepSeek-R1. The design ID will be pre-populated. |
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4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). |
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5. For Number of circumstances, get in a variety of [circumstances](http://gitea.digiclib.cn801) (in between 1-100). |
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6. For example type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. |
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Optionally, you can configure sophisticated security and infrastructure settings, including virtual [private](http://118.25.96.1183000) cloud (VPC) networking, service role consents, and encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you may desire to review these settings to line up with your organization's security and compliance requirements. |
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7. Choose Deploy to start using the design.<br> |
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<br>When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play ground to access an interactive interface where you can try out different triggers and adjust design parameters like temperature level and optimum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, content for inference.<br> |
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<br>This is an exceptional way to explore the design's reasoning and text generation abilities before [incorporating](https://www.truckjob.ca) it into your applications. The play ground supplies instant feedback, helping you understand how the [model reacts](https://flowndeveloper.site) to various inputs and letting you tweak your triggers for optimum outcomes.<br> |
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<br>You can quickly evaluate the design in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br> |
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<br>Run inference using guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing 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 developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends a demand to produce text based upon 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, integrated algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and 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 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist 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 actions to deploy 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 prompted to develop a domain. |
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3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br> |
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<br>The design internet browser displays available models, with details like the service provider name and model abilities.<br> |
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. |
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Each design card shows crucial details, consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- Task classification (for example, Text Generation). |
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Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design<br> |
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<br>5. Choose the model card to view 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 service provider 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 specifications. |
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- Usage guidelines<br> |
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<br>Before you release the model, it's recommended to review the [design details](http://filmmaniac.ru) and license terms to confirm compatibility with your use case.<br> |
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<br>6. Choose Deploy to proceed with implementation.<br> |
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<br>7. For Endpoint name, utilize the immediately produced name or produce a custom-made one. |
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8. For Instance type ¸ pick 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 suitable circumstances types and counts is essential for cost and efficiency optimization. Monitor [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:DenishaHolyfield) your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. |
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10. Review all setups for accuracy. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. |
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11. Choose Deploy to deploy the model.<br> |
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<br>The release process can take numerous minutes to complete.<br> |
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<br>When [implementation](https://git.xedus.ru) is complete, your endpoint status will alter to InService. At this moment, the design is all set to accept inference requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, you can invoke the design using a SageMaker runtime [customer](https://1samdigitalvision.com) and incorporate 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 require to install the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is provided in the Github here. You can clone the notebook and range from SageMaker Studio.<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 produce a guardrail utilizing the Amazon Bedrock console or the API, and [implement](http://120.48.141.823000) it as displayed in the following code:<br> |
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<br>Clean up<br> |
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<br>To prevent unwanted charges, finish the actions in this section to clean up your resources.<br> |
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<br>Delete the Amazon Bedrock [Marketplace](https://nytia.org) implementation<br> |
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<br>If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following actions:<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 releases section, locate the endpoint you wish to delete. |
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3. Select the endpoint, and on the Actions menu, select Delete. |
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4. Verify the [endpoint details](http://107.182.30.1906000) to make certain you're erasing the appropriate deployment: 1. [Endpoint](https://77.248.49.223000) name. |
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2. Model name. |
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3. [Endpoint](http://1.94.127.2103000) 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 expenses 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 [yewiki.org](https://www.yewiki.org/User:WinifredHassell) 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock [tooling](http://hmind.kr) with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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](http://test.wefanbot.com3000) at AWS. He assists emerging generative [AI](https://giaovienvietnam.vn) business construct innovative options utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the reasoning performance of large language designs. In his leisure time, Vivek takes pleasure in hiking, viewing motion pictures, and [attempting](http://8.129.8.58) various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://sportify.brandnitions.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://corerecruitingroup.com) [accelerators](http://193.30.123.1883500) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br> |
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<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://www.ggram.run) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.prime.cv) hub. She is enthusiastic about constructing solutions that help consumers accelerate their [AI](https://git.wyling.cn) journey and unlock company worth.<br> |
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