Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are delighted 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 [deploy DeepSeek](https://dev.fleeped.com) [AI](http://www.machinekorea.net)'s first-generation frontier model, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](http://new-delhi.rackons.com) concepts 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](https://gogs.dzyhc.com) to deploy the distilled variations of the designs also.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](http://81.71.148.57:8080) that uses reinforcement finding out to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its reinforcement learning (RL) step, which was utilized to fine-tune the design's reactions beyond the basic pre-training and [wiki.whenparked.com](https://wiki.whenparked.com/User:MaxRountree) tweak procedure. By [including](https://gps-hunter.ru) RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's equipped to break down complex questions and factor through them in a detailed way. This directed thinking process permits the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the industry's attention as a flexible [text-generation design](http://a21347410b.iask.in8500) that can be [incorporated](https://www.womplaz.com) into various workflows such as agents, rational reasoning and [data analysis](http://qiriwe.com) tasks.<br> |
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<br>DeepSeek-R1 [utilizes](https://videofrica.com) a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, making it possible for effective reasoning by routing inquiries to the most appropriate professional "clusters." This method permits the model to focus on various issue domains while maintaining total effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, using 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 design, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and assess designs against key safety criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](http://gogs.gzzzyd.com) applications.<br> |
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<br>Prerequisites<br> |
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<br>To [release](http://52.23.128.623000) the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge [circumstances](https://omegat.dmu-medical.de) in the AWS Region you are releasing. To ask for a limit increase, produce a limitation increase demand and reach out 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 Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Set up permissions 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 permits you to introduce safeguards, prevent hazardous content, and evaluate models against essential security requirements. You can execute safety steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon [Bedrock console](http://111.231.76.912095) or the API. For the example code to produce the guardrail, see the GitHub repo.<br> |
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<br>The general circulation includes the following steps: 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 design for reasoning. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the final 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. 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 gives you access to over 100 popular, emerging, and specialized structure models (FMs) through [Amazon Bedrock](https://forum.batman.gainedge.org). 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 models in the navigation pane. |
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At the time of writing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.<br> |
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<br>The design detail page supplies essential details about the model's capabilities, pricing structure, and execution guidelines. You can discover detailed usage directions, [consisting](https://git.augustogunsch.com) of sample API calls and code bits for combination. The model supports numerous text generation tasks, including content creation, code generation, and concern answering, using its support finding out optimization and CoT thinking capabilities. |
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The page likewise consists of deployment choices and licensing details to help you begin with DeepSeek-R1 in your [applications](https://equipifieds.com). |
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3. To begin using DeepSeek-R1, choose 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](https://smartcampus-seskoal.id) characters). |
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5. For Number of circumstances, enter a variety of circumstances (between 1-100). |
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6. For example type, select your circumstances type. For optimal performance with DeepSeek-R1, [wavedream.wiki](https://wavedream.wiki/index.php/User:VeroniqueBernhar) a GPU-based instance type like ml.p5e.48 xlarge is recommended. |
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Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, [service](http://logzhan.ticp.io30000) role approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production releases, you might want to examine these settings to line up with your [company's security](https://tempjobsindia.in) and compliance requirements. |
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7. Choose Deploy to begin utilizing 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 playground. |
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8. Choose Open in play ground to access an interactive interface where you can try out different triggers and change design criteria like temperature level and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, content for inference.<br> |
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<br>This is an excellent way to explore the model's thinking and text generation capabilities before incorporating it into your applications. The play ground offers immediate feedback, helping you understand how the design reacts to various inputs and letting you tweak your triggers for ideal results.<br> |
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<br>You can rapidly test 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 reasoning using guardrails with the deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to carry out reasoning utilizing 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](https://salesupprocess.it) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a demand to produce text based upon 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 options that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 design through SageMaker JumpStart uses two hassle-free approaches: utilizing the user-friendly SageMaker JumpStart UI or [implementing programmatically](http://football.aobtravel.se) through the SageMaker Python SDK. Let's explore both techniques to help you pick the approach that [finest fits](http://git.daiss.work) 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 using 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](https://community.scriptstribe.com) to create 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 browser displays available models, with details like the provider name and design capabilities.<br> |
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. |
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Each model card shows essential details, consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- [Task category](https://techtalent-source.com) (for instance, Text Generation). |
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Bedrock Ready badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to [conjure](https://www.indianhighcaste.com) up the model<br> |
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<br>5. Choose the design card to see the model details page.<br> |
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<br>The model details page [consists](https://ehrsgroup.com) 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 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 requirements. |
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- Usage guidelines<br> |
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<br>Before you release the model, it's suggested to examine the model details and license terms to verify compatibility with your usage case.<br> |
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<br>6. Choose Deploy to continue with implementation.<br> |
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<br>7. For Endpoint name, utilize the instantly created name or produce a custom-made one. |
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8. For example [type ¸](http://221.131.119.210030) select an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial instance count, get in the number of circumstances (default: 1). |
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Selecting appropriate circumstances types and counts is essential for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. |
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10. Review all configurations for accuracy. For this model, 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 a number of minutes to complete.<br> |
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<br>When release is complete, your endpoint status will alter to InService. At this point, the model is prepared to accept reasoning requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is total, you can conjure up the model using a SageMaker runtime customer 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 going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS authorizations 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 the model is provided in the Github here. You can clone the [notebook](https://wiki.rolandradio.net) 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 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 [produce](https://kod.pardus.org.tr) a [guardrail](https://mediawiki1334.00web.net) using the Amazon Bedrock console or the API, and execute it as displayed in the following code:<br> |
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<br>Clean up<br> |
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<br>To avoid undesirable charges, finish the steps in this section to clean up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace implementation<br> |
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<br>If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations. |
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2. In the Managed implementations section, locate 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 deleting the appropriate 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 design you released 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 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 Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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 at AWS. He helps emerging generative [AI](https://seedvertexnetwork.co.ke) business build ingenious services utilizing AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of big [language](https://git.maxwellj.xyz) models. In his spare time, Vivek delights in treking, enjoying films, and attempting different foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://mssc.ltd) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://209.87.229.34:7080) [accelerators](https://www.ksqa-contest.kr) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://git.boergmann.it) with the Third-Party Model Science team at AWS.<br> |
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<br>[Banu Nagasundaram](https://www.mapsisa.org) leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://git.panggame.com) [AI](https://www.jpaik.com) hub. She is passionate about constructing solutions that help clients accelerate their [AI](https://namoshkar.com) journey and unlock business value.<br> |
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