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<br>Today, we are thrilled to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://gogs.fundit.cn:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](http://git.maxdoc.top) concepts 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 actions to deploy the distilled variations 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://git.biosens.rs) that uses reinforcement discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying feature is its support knowing (RL) step, which was used to improve the design's responses beyond the basic pre-training and [tweak procedure](http://forum.infonzplus.net). By [incorporating](https://okoskalyha.hu) RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate questions and reason through them in a detailed way. This assisted reasoning procedure allows the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation model that can be integrated into various workflows such as agents, logical reasoning and information analysis tasks.<br> |
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables [activation](http://13.209.39.13932421) of 37 billion criteria, allowing effective reasoning by routing queries to the most appropriate expert "clusters." This method allows the design to concentrate on different problem domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of [HBM memory](https://rootsofblackessence.com) in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy 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 models bring the reasoning abilities 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 efficient models to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.<br> |
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<br>You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and examine designs against crucial safety criteria. At the time of [writing](http://121.40.114.1279000) this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and use them to the DeepSeek-R1 model, [enhancing](http://damoa8949.com) user experiences and standardizing safety controls across your generative [AI](https://lr-mediconsult.de) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas [console](https://src.strelnikov.xyz) and under AWS Services, [pick Amazon](https://wiki.ragnaworld.net) 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 limit increase, develop a limitation boost request and connect to your account team.<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) consents to use Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use 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 enables you to present safeguards, prevent hazardous material, and assess models against key security requirements. You can implement precaution for the DeepSeek-R1 [design utilizing](http://geoje-badapension.com) the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and design responses released on [Amazon Bedrock](https://39.98.119.14) Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.<br> |
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<br>The basic flow includes the following steps: First, the system [receives](https://sondezar.com) 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 design for inference. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the [final outcome](https://git.programming.dev). However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073259) whether it happened at the input or output phase. The examples showcased in the following areas show reasoning 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 foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. |
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At the time of composing this post, you can use the InvokeModel API to [conjure](https://git.perrocarril.com) up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.<br> |
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<br>The model detail page offers vital details about the design's capabilities, prices structure, and execution guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for integration. The design supports numerous text generation tasks, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:FabianQ0253599) consisting of material development, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking abilities. |
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The page also consists of deployment alternatives and licensing details to help you start with DeepSeek-R1 in your applications. |
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3. To start using DeepSeek-R1, choose Deploy.<br> |
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<br>You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. |
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4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Number of circumstances, enter a number of instances (between 1-100). |
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6. For example type, choose your circumstances type. For ideal efficiency with DeepSeek-R1, a [GPU-based circumstances](https://jobsportal.harleysltd.com) type like ml.p5e.48 xlarge is advised. |
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Optionally, you can set up advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, [service role](http://krasnoselka.od.ua) approvals, and encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might wish to examine these [settings](http://47.93.56.668080) to line up with your company'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 check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. |
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8. Choose Open in play area to access an interactive user interface where you can try out various prompts and adjust model criteria like temperature level and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, material for reasoning.<br> |
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<br>This is an excellent way to explore the design's reasoning and text generation abilities before integrating it into your applications. The playground provides instant feedback, assisting you understand how the design reacts to various inputs and letting you fine-tune your triggers for [optimum](https://dev.ncot.uk) results.<br> |
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<br>You can quickly test the model in the play ground through the UI. However, to conjure up the released 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 deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to carry out reasoning utilizing a [deployed](http://47.120.16.1378889) DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:GayKastner43699) the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends out a request to [generate text](https://git.hmmr.ru) 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 deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, [wiki-tb-service.com](http://wiki-tb-service.com/index.php?title=Benutzer:TobiasChristison) and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) deploy them into production using 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 intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you select the technique 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 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 triggered to create a domain. |
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3. On the SageMaker Studio console, pick in the navigation pane.<br> |
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<br>The model internet browser displays available designs, with details like the provider 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 reveals essential 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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Bedrock Ready badge (if suitable), [indicating](https://app.deepsoul.es) that this design can be [registered](http://git.foxinet.ru) with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model<br> |
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<br>5. Choose the [model card](https://scfr-ksa.com) 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 model name and company details. |
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Deploy button to release the design. |
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About and Notebooks tabs with detailed details<br> |
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<br>The About tab includes crucial 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 deploy the design, it's suggested to examine 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 deployment.<br> |
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<br>7. For Endpoint name, use the immediately created name or produce a customized one. |
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8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge). |
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9. For Initial instance count, enter the number of circumstances (default: 1). |
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Selecting suitable instance types and counts is important for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, [Real-time reasoning](https://www.meetgr.com) is chosen by default. This is optimized for sustained traffic and [raovatonline.org](https://raovatonline.org/author/dixietepper/) low latency. |
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10. Review all setups for precision. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. |
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11. Choose Deploy to release the model.<br> |
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<br>The deployment procedure can take numerous minutes to complete.<br> |
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<br>When release 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 keep an eye on the deployment development on the SageMaker console Endpoints page, which will show [pertinent](https://dongawith.com) metrics and status details. When the implementation is total, you can invoke the design using a SageMaker runtime customer and incorporate it with your applications.<br> |
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<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br> |
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<br>To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing 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 additional demands 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 develop a guardrail using the Amazon Bedrock [console](https://jobspaddy.com) or the API, and implement it as shown in the following code:<br> |
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<br>Tidy up<br> |
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<br>To avoid unwanted charges, finish the steps in this section to tidy up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace deployment<br> |
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<br>If you released the model 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, select Marketplace releases. |
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2. In the Managed releases section, find the [endpoint](https://lensez.info) 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](https://dakresources.com) to make certain you're erasing the proper 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 model you released will sustain costs if you leave it running. Use the following code to erase 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 design 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 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 helps emerging generative [AI](http://82.157.77.120:3000) business develop innovative options utilizing AWS services and accelerated calculate. Currently, he is focused on establishing strategies for fine-tuning and optimizing the inference efficiency of large language models. In his leisure time, Vivek delights in treking, viewing movies, and trying various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://120.237.152.218:8888) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://dongawith.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://dongawith.com) with the Third-Party Model Science group 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](http://www.gbape.com) center. She is passionate about building services that assist clients accelerate their [AI](http://metis.lti.cs.cmu.edu:8023) journey and unlock organization value.<br> |
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