Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart
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DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md
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DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md
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<br>Today, we are excited to announce that DeepSeek R1 distilled Llama and [Qwen designs](https://39.105.45.141) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://lonestartube.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:Anita45916100577) responsibly scale your generative [AI](https://git.iidx.ca) ideas 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](http://120.196.85.1743000). You can follow similar steps to deploy the distilled versions of the models too.<br>
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<br>Overview of DeepSeek-R1<br>
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<br>DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://www.celest-interim.fr) that utilizes reinforcement discovering to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing function is its reinforcement learning (RL) step, which was used to improve the design's actions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, indicating it's geared up to break down intricate inquiries and reason through them in a detailed way. This directed reasoning [process](http://163.66.95.1883001) allows the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation model that can be incorporated into various workflows such as representatives, sensible thinking and data interpretation tasks.<br>
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, enabling efficient reasoning by routing queries to the most appropriate specialist "clusters." This technique allows the model to specialize in different issue domains while maintaining general performance. DeepSeek-R1 requires a minimum of 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 supplying](http://fcgit.scitech.co.kr) 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more efficient architectures based upon [popular](http://123.207.206.1358048) 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 designs to mimic the habits and thinking patterns of the larger DeepSeek-R1 design, using it as an instructor design.<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 releasing this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and examine designs against crucial safety requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](http://39.105.129.2293000) only the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](http://47.93.16.222:3000) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're [utilizing](http://park7.wakwak.com) ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limit increase, produce a limitation increase demand and connect to your account team.<br>
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<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Establish consents 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 allows you to introduce safeguards, prevent damaging material, and assess designs against crucial security requirements. You can carry out safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.<br>
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<br>The general circulation involves the following steps: First, the system receives 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 model for reasoning. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the last 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 [occurred](https://neejobs.com) at the input or output phase. The examples showcased in the following areas demonstrate 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 provides you access to over 100 popular, emerging, [wiki.eqoarevival.com](https://wiki.eqoarevival.com/index.php/User:DakotaSturm5) and specialized structure models (FMs) through [Amazon Bedrock](https://coatrunway.partners). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br>
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<br>1. On the [Amazon Bedrock](https://gitlab.mnhn.lu) console, select Model catalog under Foundation models in the navigation pane.
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At the time of writing this post, you can utilize the [InvokeModel API](http://223.68.171.1508004) to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.<br>
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<br>The design detail page offers vital details about the model's capabilities, rates structure, and application guidelines. You can find detailed use directions, consisting of sample API calls and code bits for combination. The design supports different text generation jobs, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:Kraig8717051) consisting of material development, code generation, and concern answering, using its reinforcement learning optimization and [CoT reasoning](http://repo.jd-mall.cn8048) capabilities.
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The page likewise consists of deployment options and licensing details to assist you get going with DeepSeek-R1 in your applications.
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3. To start using DeepSeek-R1, select 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 (between 1-50 alphanumeric characters).
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5. For Number of circumstances, get in a variety of circumstances (in between 1-100).
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6. For Instance type, choose your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
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Optionally, you can configure sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, [service role](http://b-ways.sakura.ne.jp) authorizations, and encryption settings. For most cases, the default settings will work well. However, for [production](http://images.gillion.com.cn) releases, you may want to examine these settings to line up with your organization's security and compliance requirements.
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7. Choose Deploy to start using the model.<br>
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<br>When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
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8. Choose Open in play ground to access an interactive interface where you can explore various triggers and adjust model criteria like temperature level and optimum length.
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For instance, content for inference.<br>
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<br>This is an exceptional method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play ground provides instant feedback, assisting you understand how the design responds to numerous inputs and letting you tweak your prompts for optimum outcomes.<br>
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<br>You can quickly evaluate the design in the play ground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
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<br>Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to perform inference using a released DeepSeek-R1 design through [Amazon Bedrock](https://messengerkivu.com) utilizing the invoke_model and ApplyGuardrail API. You can [develop](https://careers.ebas.co.ke) a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends a demand [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Sherman25U) to create 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) hub with FMs, built-in algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 convenient approaches: using the [user-friendly SageMaker](https://villahandle.com) JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you select the technique that finest fits your requirements.<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 utilizing SageMaker JumpStart:<br>
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<br>1. On the SageMaker console, select Studio in the navigation pane.
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2. First-time users will be triggered to create a domain.
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3. On the [SageMaker Studio](https://www.cupidhive.com) console, pick JumpStart in the navigation pane.<br>
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<br>The model browser shows available designs, with details like the [supplier](http://47.110.248.4313000) name and design capabilities.<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 key 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 appropriate), suggesting that this design can be [registered](https://jobs.ethio-academy.com) with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke 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 release 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 guidelines<br>
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<br>Before you deploy the design, it's recommended to review the model details and license terms to validate compatibility with your use case.<br>
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<br>6. Choose Deploy to proceed with release.<br>
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<br>7. For Endpoint name, utilize the instantly generated name or produce a custom one.
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8. For Instance type ¸ choose an [instance type](https://git.saphir.one) (default: ml.p5e.48 xlarge).
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9. For Initial instance count, go into the number of circumstances (default: 1).
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Selecting proper circumstances types and counts is crucial for expense and efficiency optimization. [Monitor](https://social.nextismyapp.com) your implementation to adjust these settings as needed.Under [Inference](https://git.whitedwarf.me) type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
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10. Review all configurations for accuracy. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
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11. Choose Deploy to deploy the design.<br>
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<br>The implementation procedure can take a number of minutes to complete.<br>
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<br>When release is complete, your [endpoint status](https://git.jerl.dev) will alter to [InService](http://120.77.67.22383). At this moment, the model is ready to accept inference requests through the [endpoint](http://www.sa1235.com). You can keep track of the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime client and integrate 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 start with DeepSeek-R1 using the SageMaker Python SDK, you will [require](http://gitlab.ifsbank.com.cn) to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:ArethaClapp560) environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for [yewiki.org](https://www.yewiki.org/User:Soila468300687) inference programmatically. The code for [deploying](https://taelimfwell.com) the design is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.<br>
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<br>You can run [additional requests](https://satitmattayom.nrru.ac.th) 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:<br>
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<br>Tidy up<br>
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<br>To prevent undesirable charges, finish the actions in this area to tidy up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace release<br>
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<br>If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:<br>
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations.
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2. In the Managed implementations area, locate the [endpoint](http://wiki.pokemonspeedruns.com) you want to delete.
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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 proper implementation: 1. [Endpoint](https://carepositive.com) name.
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2. Model name.
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3. Endpoint status<br>
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<br>Delete the SageMaker JumpStart predictor<br>
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<br>The SageMaker JumpStart design you released will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and [Resources](https://newvideos.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 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, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:AnnisGarret) refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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://epcblind.org) companies construct innovative services utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing strategies for fine-tuning and [optimizing](http://203.171.20.943000) the [inference performance](https://git.fracturedcode.net) of big language designs. In his free time, Vivek enjoys hiking, seeing movies, and trying different foods.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.skyviewfund.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://git.bzgames.cn) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
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<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://kollega.by) with the Third-Party Model Science group at AWS.<br>
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<br>[Banu Nagasundaram](https://git.prime.cv) leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gogs.greta.wywiwyg.net) hub. She is passionate about [constructing services](http://park7.wakwak.com) that assist clients accelerate their [AI](https://corvestcorp.com) journey and unlock organization worth.<br>
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