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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<br>Today, we are thrilled 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 deploy DeepSeek [AI](https://work.melcogames.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](http://1.92.128.200:3000) concepts on AWS.<br>
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<br>In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps 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](https://zeroth.one) that utilizes support learning to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its reinforcement learning (RL) step, which was used to fine-tune the model's responses beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's equipped to break down complex queries and reason through them in a detailed manner. This guided reasoning procedure permits the design to produce more accurate, transparent, and [detailed answers](http://gitlab.awcls.com). This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the [market's attention](https://gitea.easio-com.com) as a flexible text-generation model that can be integrated into various workflows such as representatives, logical thinking and information analysis jobs.<br>
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion [specifications](http://git.sinoecare.com) in size. The MoE architecture permits activation of 37 billion specifications, enabling effective inference by [routing questions](https://armconnection.com) to the most relevant expert "clusters." This [method permits](https://talentocentroamerica.com) the design to concentrate on various issue domains while [maintaining](https://git.youxiner.com) general performance. DeepSeek-R1 requires a minimum of 800 GB of [HBM memory](https://www.ifodea.com) in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more effective architectures based on [popular](http://20.198.113.1673000) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient designs to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher design.<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 design, we [recommend releasing](https://mypungi.com) this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and assess designs against essential safety criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, [enhancing](https://smartcampus-seskoal.id) user experiences and standardizing security controls throughout your generative [AI](http://gogs.black-art.cn) 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, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:ColleenLankford) choose Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To request a limit increase, develop a limit increase demand and reach out to your account team.<br>
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and [Gain Access](https://www.ubom.com) To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish permissions 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 enables you to introduce safeguards, prevent hazardous material, and examine designs against key safety criteria. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.<br>
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<br>The general circulation [involves](https://career.logictive.solutions) the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. 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 happened 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 gives you access to over 100 popular, emerging, and [specialized structure](https://www.kenpoguy.com) 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 catalog under Foundation designs in the navigation pane.
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At the time of writing 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 service provider and pick the DeepSeek-R1 model.<br>
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<br>The design detail page offers important details about the design's abilities, prices structure, and execution guidelines. You can discover detailed use guidelines, consisting of [sample API](http://yijichain.com) calls and code bits for integration. The design supports different text generation jobs, [including material](http://git.cnibsp.com) creation, code generation, and question answering, utilizing its support learning optimization and CoT reasoning abilities.
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The page also includes deployment alternatives and licensing details to help you begin 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 to set up the implementation details 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 instances, get in a number of circumstances (in between 1-100).
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6. For Instance type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a [GPU-based circumstances](https://git.uucloud.top) type like ml.p5e.48 xlarge is recommended.
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Optionally, you can configure sophisticated [security](https://asromafansclub.com) and infrastructure settings, including virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may want 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 deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
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8. Choose Open in playground to access an interactive interface where you can explore various prompts and adjust design parameters like temperature and maximum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For example, material for reasoning.<br>
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<br>This is an excellent method to check out the design's thinking and text generation capabilities before integrating it into your [applications](https://www.highpriceddatinguk.com). The play area supplies instant feedback, helping you comprehend how the design reacts to numerous inputs and letting you fine-tune your triggers for optimal outcomes.<br>
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<br>You can rapidly test the design in the play area through the UI. However, to invoke the deployed 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 released DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock [console](https://openedu.com) or the API. For the example code to [produce](https://code.smolnet.org) the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends out a demand to create text 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can [release](http://tian-you.top7020) with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the approach that finest fits 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 deploy DeepSeek-R1 using SageMaker JumpStart:<br>
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<br>1. On the SageMaker console, select Studio in the navigation pane.
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2. First-time users will be prompted to produce 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 model web browser displays available models, with details like the [supplier](https://cruyffinstitutecareers.com) name and design abilities.<br>
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
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Each model card shows key details, consisting of:<br>
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<br>- Model name
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- Provider name
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- Task [classification](https://git.didi.la) (for instance, Text Generation).
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Bedrock Ready badge (if appropriate), indicating 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 design card to view 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 [provider details](http://139.9.60.29).
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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 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 advised to examine the model details and license terms to validate 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, use the automatically generated name or develop a custom-made one.
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8. For example [type ¸](https://newnormalnetwork.me) select an instance type (default: ml.p5e.48 xlarge).
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9. For Initial instance count, enter the number of instances (default: 1).
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Selecting suitable instance types and counts is important for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
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10. Review all configurations for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
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11. Choose Deploy to release the design.<br>
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<br>The release procedure can take a number of minutes to finish.<br>
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<br>When implementation is complete, your endpoint status will change to InService. At this moment, the model is prepared to accept inference demands through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the design using a SageMaker runtime customer 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 get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS consents 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 deploying the design 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 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a [guardrail](http://121.5.25.2463000) using the Amazon Bedrock console or the API, and [implement](https://ruraltv.co.za) it as shown in the following code:<br>
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<br>Clean up<br>
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<br>To prevent undesirable charges, finish the [actions](https://youarealways.online) in this section to clean up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace release<br>
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<br>If you deployed the model 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 deployments.
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2. In the Managed deployments section, find the endpoint you wish 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 erasing the appropriate release: 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 released will sustain costs 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 checked out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. [Visit SageMaker](https://jobs.alibeyk.com) JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with [Amazon SageMaker](https://git.gday.express) JumpStart models, SageMaker JumpStart pretrained models, 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 helps emerging generative [AI](http://115.159.107.117:3000) business construct ingenious options utilizing AWS services and accelerated compute. Currently, he is on [developing methods](http://forum.altaycoins.com) for fine-tuning and enhancing the reasoning efficiency of large language designs. In his leisure time, Vivek takes pleasure in hiking, seeing motion pictures, and attempting various cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://it-viking.ch) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](http://git.motr-online.com) of focus is AWS [AI](https://git.tx.pl) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and [Bioinformatics](http://47.101.207.1233000).<br>
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<br>Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://test-www.writebug.com:3000) with the Third-Party Model Science team 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](https://git.brodin.rocks) hub. She is passionate about constructing solutions that assist consumers accelerate their [AI](https://gitea.createk.pe) journey and unlock organization value.<br>
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