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<br>Today, we are delighted to announce that DeepSeek R1 distilled Llama and [Qwen designs](https://www.flytteogfragttilbud.dk) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://willingjobs.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](https://yourmoove.in) concepts on AWS.<br> |
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<br>In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy 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 big language design (LLM) developed by DeepSeek [AI](https://jobwings.in) that utilizes support [learning](https://carvidoo.com) to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating feature is its support learning (RL) action, which was utilized to fine-tune the design's responses beyond the [basic pre-training](https://git.bubbleioa.top) and [fine-tuning process](https://prosafely.com). By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate queries and reason through them in a detailed way. This assisted thinking procedure permits the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be integrated into various workflows such as representatives, sensible reasoning and [data interpretation](https://tobesmart.co.kr) jobs.<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 permits activation of 37 billion specifications, allowing effective inference by routing [queries](https://giftconnect.in) to the most pertinent specialist "clusters." This technique allows the model to [specialize](http://git.tbd.yanzuoguang.com) in various problem domains while maintaining overall effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance 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 models bring the thinking abilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective models to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 design, using 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://skytechenterprisesolutions.net) model, we advise deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and examine designs against key safety requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](https://git.perrocarril.com) 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, select Amazon SageMaker, and verify you're utilizing 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 deploying. To request a limitation increase, create a limitation increase demand 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 appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon [Bedrock Guardrails](https://twitemedia.com). For guidelines, see Establish consents to utilize guardrails for content filtering.<br> |
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<br>Implementing guardrails with the ApplyGuardrail API<br> |
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<br>Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging content, and assess designs against key safety requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to [evaluate](https://www.klartraum-wiki.de) user inputs and [design actions](https://wiki.kkg.org) released on Amazon Bedrock [Marketplace](https://gitea.ci.apside-top.fr) and SageMaker JumpStart. You can develop 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 basic circulation includes the following actions: First, 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 model for inference. After getting the model's output, another guardrail check is applied. 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 indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas 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 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, choose Model catalog under Foundation models in the navigation pane. |
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At the time of writing this post, you can use the InvokeModel API to conjure up 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 offers necessary details about the design's capabilities, rates structure, and execution guidelines. You can discover detailed use instructions, consisting of sample API calls and code snippets for integration. The model supports various text generation jobs, including content development, code generation, and concern answering, utilizing its [reinforcement discovering](http://161.97.176.30) optimization and CoT thinking capabilities. |
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The page likewise consists of implementation alternatives and licensing details to assist you get begun with DeepSeek-R1 in your applications. |
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3. To begin using DeepSeek-R1, select Deploy.<br> |
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<br>You will be prompted to configure the [implementation details](https://www.jobs.prynext.com) 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 Number of circumstances, go into a variety of circumstances (in between 1-100). |
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6. For Instance type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based [instance type](http://moyora.today) like ml.p5e.48 xlarge is advised. |
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Optionally, you can [configure sophisticated](http://git.jaxc.cn) security and [infrastructure](https://www.securityprofinder.com) settings, consisting of virtual personal cloud (VPC) networking, service role permissions, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, 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 begin using the model.<br> |
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<br>When the deployment is complete, you can test 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 prompts and adjust design criteria like temperature level and maximum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For example, material for reasoning.<br> |
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<br>This is an outstanding way to explore the model's reasoning and text generation abilities before integrating it into your applications. The playground offers immediate feedback, assisting you understand how the [model reacts](https://gitlab.companywe.co.kr) to various inputs and letting you tweak your prompts for optimum outcomes.<br> |
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<br>You can rapidly evaluate the model in the play ground 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 deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to perform 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 develop the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up [reasoning](https://insta.tel) criteria, 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, built-in algorithms, and prebuilt ML [services](https://epspatrolscv.com) that you can release with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://hyped4gamers.com) models to your use 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 2 hassle-free methods: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the method that finest 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 actions to deploy DeepSeek-R1 [utilizing SageMaker](https://app.galaxiesunion.com) 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 produce a domain. |
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3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br> |
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<br>The design internet browser shows available designs, with details like the supplier name and model capabilities.<br> |
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 design 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 appropriate), suggesting that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model<br> |
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<br>5. Choose the design card to view the design details page.<br> |
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<br>The design details page consists of the following details:<br> |
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<br>- The design name and service 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 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 release the design, it's recommended to review the model details and license terms to [verify compatibility](https://134.209.236.143) with your usage case.<br> |
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<br>6. Choose Deploy to continue with release.<br> |
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<br>7. For Endpoint name, use the instantly created name or create a custom one. |
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8. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, enter the number of instances (default: 1). |
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Selecting proper circumstances types and counts is important for cost and efficiency optimization. Monitor your implementation to adjust these [settings](http://146.148.65.983000) as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and [low latency](https://skytube.skyinfo.in). |
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10. Review all configurations for precision. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. |
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11. Choose Deploy to deploy the model.<br> |
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<br>The deployment process can take several minutes to complete.<br> |
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<br>When implementation is complete, your endpoint status will alter to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can conjure up the design utilizing a SageMaker runtime client 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 start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a [detailed](http://121.36.62.315000) code example that shows how to deploy and utilize DeepSeek-R1 for [reasoning programmatically](https://git.liubin.name). The code for deploying the design is supplied 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 demands 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 [predictor](http://jobpanda.co.uk). You can create 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 prevent unwanted charges, complete the actions in this section to tidy up your [resources](http://42.192.69.22813000).<br> |
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<br>Delete the Amazon Bedrock [Marketplace](https://qademo2.stockholmitacademy.org) deployment<br> |
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<br>If you released the design using Amazon Bedrock Marketplace, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments. |
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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, select Delete. |
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4. Verify the endpoint details to make certain you're erasing the appropriate 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 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](https://www.acaclip.com) and [Resources](https://www.finceptives.com).<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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker [JumpStart Foundation](https://git.vincents.cn) Models, Amazon Bedrock Marketplace, and Beginning 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://jobs1.unifze.com) business build [ingenious solutions](https://app.galaxiesunion.com) using AWS services and sped up calculate. Currently, he is concentrated on developing methods for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) fine-tuning and enhancing the reasoning efficiency of large language models. In his downtime, Vivek delights in hiking, watching movies, and trying various cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://wiki.cemu.info) Specialist Solutions Architect with the Third-Party Model [Science](http://47.102.102.152) team at AWS. His area of focus is AWS [AI](https://yaseen.tv) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://git.isatho.me) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gochacho.com) hub. She is enthusiastic about developing solutions that help customers accelerate their [AI](https://b52cum.com) journey and unlock company value.<br> |
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