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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 release DeepSeek [AI](http://115.29.48.48:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://olymponet.com) ideas on AWS.<br>
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<br>In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the [distilled versions](https://gitea.qianking.xyz3443) 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) established by DeepSeek [AI](http://39.104.23.77:3000) that uses reinforcement finding out to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An [essential](https://cagit.cacode.net) identifying function is its support learning (RL) step, which was utilized to fine-tune the model's actions beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually improving both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's equipped to break down intricate queries and reason through them in a detailed manner. This assisted reasoning process allows the design to produce more precise, transparent, and detailed answers. This design integrates [RL-based fine-tuning](https://www.ycrpg.com) with CoT capabilities, aiming to create structured responses while [focusing](https://play.sarkiniyazdir.com) on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be incorporated into different workflows such as agents, rational thinking and information interpretation jobs.<br>
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling effective inference by routing queries to the most relevant professional "clusters." This technique allows the model to focus on various issue domains while maintaining overall efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 [xlarge instance](https://juryi.sn) to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 [distilled](http://www.larsaluarna.se) designs bring the thinking capabilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective models to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor design.<br>
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<br>You can [release](http://106.52.242.1773000) DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and assess models against crucial security requirements. At the time of [writing](http://82.157.11.2243000) 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 various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://gallery.wideworldvideo.com) applications.<br>
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<br>Prerequisites<br>
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<br>To release 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 [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:EleanorBerry902) under AWS Services, choose Amazon SageMaker, and verify you're using 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 limitation increase, create a limitation increase request and connect to your account group.<br>
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<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1009888) Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Establish approvals to utilize 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 you to present safeguards, avoid damaging content, and examine designs against essential safety requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model reactions 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 create the guardrail, see the GitHub repo.<br>
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<br>The basic flow involves the following actions: First, the system [receives](https://avpro.cc) an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for [inference](https://inspirationlift.com). After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the final result. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate inference using this API.<br>
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
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<br>Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through [Amazon Bedrock](https://boonbac.com). 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, pick Model catalog under Foundation designs in the navigation pane.
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At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.<br>
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<br>The design detail page offers essential details about the design's abilities, prices structure, and application standards. You can discover detailed usage guidelines, including sample API calls and code bits for combination. The model supports different text generation tasks, including material creation, code generation, and concern answering, using its reinforcement finding out optimization and CoT thinking abilities.
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The page likewise includes implementation alternatives and licensing details to help you get started with DeepSeek-R1 in your applications.
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3. To begin utilizing DeepSeek-R1, pick Deploy.<br>
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<br>You will be [triggered](https://git.apps.calegix.net) to set up the deployment details for DeepSeek-R1. The model 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 circumstances, enter a variety of circumstances (between 1-100).
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6. For example type, pick your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
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Optionally, you can set up innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For the majority of [utilize](http://yijichain.com) cases, the default settings will work well. However, for production implementations, you might wish to review these settings to line up with your company's security and compliance requirements.
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7. Choose Deploy to start using the model.<br>
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<br>When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
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8. Choose Open in playground to access an interactive user interface where you can try out different prompts and adjust design criteria like temperature and maximum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, material for inference.<br>
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<br>This is an outstanding way to check out the design's thinking and text generation abilities before integrating it into your applications. The playground provides immediate feedback, assisting you understand how the design reacts to numerous inputs and letting you tweak your triggers for optimum outcomes.<br>
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<br>You can rapidly check the design in the play area through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
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<br>Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
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<br>The following code example demonstrates how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_[runtime](https://testgitea.cldevops.de) client, configures inference parameters, and sends out a demand to [produce text](https://pattondemos.com) based on 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 [solutions](https://www.sedatconsultlimited.com) that you can [release](https://code-proxy.i35.nabix.ru) with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through [SageMaker JumpStart](http://fggn.kr) uses 2 hassle-free approaches: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques 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 prompted 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 design web browser displays available models, with details like the company name and [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:JuanitaMcGuire6) model capabilities.<br>
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<br>4. Search for DeepSeek-R1 to see 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 instance, Text Generation).
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badge (if applicable), showing that this model can be [registered](https://www.meditationgoodtip.com) with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model<br>
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<br>5. Choose the design 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 model name and [service provider](https://www.ch-valence-pro.fr) 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 essential 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 release the model, it's recommended 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 proceed with release.<br>
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<br>7. For Endpoint name, use the immediately created name or develop a [custom-made](https://ofalltime.net) one.
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8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
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9. For Initial circumstances count, get in the number of instances (default: 1).
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Selecting suitable circumstances types and counts is vital for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency.
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10. Review all setups for [surgiteams.com](https://surgiteams.com/index.php/User:AlexandraPuglies) precision. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
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11. Choose Deploy to release the model.<br>
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<br>The deployment procedure can take several 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 all set to accept reasoning demands through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your applications.<br>
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<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
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<br>To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and [utilize](http://59.110.68.1623000) DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook 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 inference 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 produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br>
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<br>Tidy 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 Marketplace implementation<br>
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<br>If you deployed the model utilizing 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, choose Marketplace [implementations](https://git.ivran.ru).
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2. In the Managed deployments area, locate the endpoint you wish to erase.
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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 correct deployment: 1. [Endpoint](https://virnal.com) 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 expenses if you leave it running. Use the following code to delete 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 [explored](http://git.indep.gob.mx) how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going 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://185.5.54.226) companies build innovative options using AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the reasoning performance of large language designs. In his downtime, Vivek enjoys hiking, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) viewing motion pictures, and trying different foods.<br>
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<br>[Niithiyn Vijeaswaran](http://forum.rcsubmarine.ru) is a Generative [AI](http://bolling-afb.rackons.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://qdate.ru) [accelerators](https://wik.co.kr) (AWS Neuron). He holds a Bachelor's degree in Computer technology and [surgiteams.com](https://surgiteams.com/index.php/User:TracyHiller54) Bioinformatics.<br>
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<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://adremcareers.com) with the Third-Party Model Science team at AWS.<br>
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<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://wikitravel.org) hub. She is enthusiastic about constructing services that help customers accelerate their [AI](http://8.140.244.224:10880) journey and unlock company value.<br>
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