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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](http://82.157.77.120:3000)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://ssconsultancy.in) ideas on AWS.<br> |
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<br>In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled versions of the models 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) established by DeepSeek [AI](https://prime-jobs.ch) that utilizes reinforcement finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement learning (RL) action, which was used to improve the design's actions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately enhancing both significance and clearness. In addition, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=998813) DeepSeek-R1 utilizes a [chain-of-thought](https://zenabifair.com) (CoT) method, suggesting it's equipped to break down complex inquiries and reason through them in a detailed way. This directed thinking process permits the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, rational thinking and information interpretation tasks.<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 criteria, enabling effective reasoning by routing questions to the most pertinent specialist "clusters." This approach allows the design to focus on various [issue domains](https://gitea.masenam.com) while maintaining overall performance. DeepSeek-R1 needs at least 800 GB of [HBM memory](https://social.nextismyapp.com) in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the reasoning 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 process of training smaller sized, more effective designs to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.<br> |
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<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in location. In this blog site, we will [utilize Amazon](http://metis.lti.cs.cmu.edu8023) Bedrock Guardrails to present safeguards, prevent hazardous content, and examine designs against crucial security criteria. At the time of composing this blog site, for DeepSeek-R1 releases on [SageMaker JumpStart](https://gitea.qianking.xyz3443) and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](https://asixmusik.com) 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 inspect 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 instance in the AWS Region you are deploying. To ask for a limit increase, develop a limit increase request and reach out to your account group.<br> |
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate [AWS Identity](https://strimsocial.net) and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Set up authorizations 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 enables you to introduce safeguards, prevent damaging material, and evaluate models against crucial security criteria. You can execute precaution for the DeepSeek-R1 design using the [Amazon Bedrock](https://www.teacircle.co.in) ApplyGuardrail API. This enables you to use guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace 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 flow [involves](https://kibistudio.com57183) 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](https://www.tqmusic.cn) check, it's sent to the model for inference. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections 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 offers you access to over 100 popular, emerging, and specialized structure 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, select Model catalog under Foundation designs in the navigation pane. |
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At the time of [writing](https://git.jzcscw.cn) this post, you can use the [InvokeModel API](https://git.gday.express) 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 [provider](https://soundfy.ebamix.com.br) and select the DeepSeek-R1 model.<br> |
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<br>The model detail page offers necessary details about the design's abilities, pricing structure, and implementation standards. You can find [detailed](https://ransomware.design) use instructions, consisting of sample API calls and code snippets for integration. The design supports numerous text generation jobs, including content production, code generation, and concern answering, using its support finding out optimization and CoT reasoning abilities. |
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The page also [consists](http://63.141.251.154) of implementation alternatives and licensing details to assist you start with DeepSeek-R1 in your applications. |
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3. To start using DeepSeek-R1, pick Deploy.<br> |
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<br>You will be prompted to set up the for DeepSeek-R1. The model 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 instances, enter a variety of circumstances (in between 1-100). |
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6. For example type, [gratisafhalen.be](https://gratisafhalen.be/author/lavondau40/) choose your [circumstances type](http://106.52.242.1773000). For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can set up innovative security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For many use 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 begin utilizing the design.<br> |
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<br>When the release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. |
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8. Choose Open in play area to access an interactive interface where you can try out different triggers and adjust model specifications 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 ideal results. For example, material for reasoning.<br> |
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<br>This is an outstanding method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play area offers immediate feedback, helping you understand how the design responds to different inputs and letting you tweak your prompts for optimal outcomes.<br> |
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<br>You can quickly evaluate the design in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br> |
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<br>Run inference using guardrails with the deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a [guardrail utilizing](http://www.localpay.co.kr) 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, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and [mediawiki.hcah.in](https://mediawiki.hcah.in/index.php?title=User:AlphonseSmallwoo) sends a request to generate text based on 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) center with FMs, built-in algorithms, and [prebuilt](https://rrallytv.com) ML [options](https://gitlab.oc3.ru) that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and 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 practical approaches: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the method that finest suits your [requirements](https://globalhospitalitycareer.com).<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following [actions](http://139.199.191.273000) to deploy 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, select JumpStart in the navigation pane.<br> |
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<br>The design browser shows available models, with details like the provider 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 category (for example, Text Generation). |
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Bedrock Ready badge (if suitable), suggesting that this model can be registered with Amazon Bedrock, enabling you to use [Amazon Bedrock](http://h2kelim.com) APIs to conjure 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 includes the following details:<br> |
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<br>- The model name and company details. |
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[Deploy button](https://www.facetwig.com) 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 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 [validate compatibility](http://lifethelife.com) 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, utilize the immediately created name or produce a custom-made one. |
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8. For example type ¸ select a [circumstances type](http://8.137.85.1813000) (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, enter the variety of instances (default: 1). |
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Selecting proper [circumstances](https://161.97.85.50) types and counts is essential for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under [Inference](https://dev.fleeped.com) type, Real-time reasoning is chosen by default. This is optimized for [sustained traffic](https://repos.ubtob.net) and low latency. |
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10. Review all [configurations](http://boiler.ttoslinux.org8888) for accuracy. For this model, we strongly suggest sticking 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 finish.<br> |
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<br>When implementation is complete, your endpoint status will change to InService. At this point, the design is all set to accept inference requests through the endpoint. You can monitor the release progress on the SageMaker [console Endpoints](https://bantooplay.com) page, which will show pertinent metrics and status details. When the release is complete, you can invoke 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 install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a [detailed](https://jobs.careersingulf.com) code example that shows how to deploy and use 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 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, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:UVGEstella) you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and [implement](https://moojijobs.com) 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](https://ransomware.design) Marketplace release<br> |
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<br>If you released the design utilizing Amazon Bedrock Marketplace, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, [pick Marketplace](https://right-fit.co.uk) implementations. |
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2. In the Managed implementations area, locate the endpoint you wish to delete. |
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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 [correct](http://chotaikhoan.me) 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 expenses if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, [yewiki.org](https://www.yewiki.org/User:FredGoble653) 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 utilizing 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, Amazon SageMaker JumpStart Foundation Models, [Amazon Bedrock](http://63.141.251.154) Marketplace, and Beginning with [Amazon SageMaker](https://git.amic.ru) 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](https://bio.rogstecnologia.com.br) business develop ingenious services using AWS [services](http://47.122.66.12910300) and sped up calculate. Currently, he is concentrated on developing strategies for fine-tuning and [optimizing](http://8.141.155.1833000) the reasoning performance of big language designs. In his free time, Vivek enjoys hiking, enjoying movies, and attempting various cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://gitea.smartscf.cn:8000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://bcde.ru) 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://collegetalks.site) 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](http://165.22.249.52:8888) center. She is enthusiastic about [constructing options](https://trabaja.talendig.com) that assist consumers accelerate their [AI](http://118.195.226.124:9000) journey and unlock service value.<br> |
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