Update '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 [release DeepSeek](https://optimaplacement.com) [AI](https://workmate.club)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://git.youxiner.com) ideas on AWS.<br> |
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<br>In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs 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) developed by DeepSeek [AI](https://code.jigmedatse.com) that uses reinforcement learning to improve thinking capabilities through a multi-stage training [procedure](http://117.72.17.1323000) from a DeepSeek-V3-Base foundation. A key differentiating feature is its support learning (RL) action, which was utilized to refine the model's actions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, meaning it's geared up to break down [complex questions](https://phoebe.roshka.com) and reason through them in a detailed manner. This assisted thinking process allows the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, rational reasoning and data interpretation tasks.<br> |
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, allowing efficient inference by routing inquiries to the most pertinent professional "clusters." This technique permits the model to specialize in various issue domains while [maintaining](http://git.anitago.com3000) total efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to [release](https://chaakri.com) the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective designs to simulate the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.<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 recommend deploying this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine designs against crucial safety criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, [Bedrock Guardrails](http://dev.zenith.sh.cn) supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety [controls](https://blogram.online) across your generative [AI](https://git.nullstate.net) [applications](https://brotato.wiki.spellsandguns.com).<br> |
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<br>Prerequisites<br> |
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<br>To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 releasing. To request a limitation increase, produce a limit increase demand and reach out 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) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals 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 permits you to introduce safeguards, avoid damaging content, and assess designs against key security requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop 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 circulation includes the following actions: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for reasoning. After getting the model's output, another guardrail check is applied. 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 phase. The examples showcased in the following sections show inference utilizing this API.<br> |
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br> |
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<br>Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through [Amazon Bedrock](http://copyvance.com). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. |
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At the time of writing this post, you can use the InvokeModel API to conjure up the model. It doesn't [support Converse](http://git.cqbitmap.com8001) APIs and other Amazon Bedrock [tooling](https://rugraf.ru). |
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2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.<br> |
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<br>The design detail page offers vital details about the design's abilities, prices structure, and [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:MajorPickering) execution standards. You can find detailed usage guidelines, including sample API calls and code bits for integration. The design supports different text generation jobs, consisting of material development, code generation, and concern answering, utilizing its support finding out optimization and CoT thinking capabilities. |
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The page likewise consists of release choices and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:LonnaGeoghegan) licensing details to assist you start with DeepSeek-R1 in your [applications](https://biiut.com). |
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3. To start using DeepSeek-R1, select Deploy.<br> |
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<br>You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be [pre-populated](https://jp.harmonymart.in). |
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4. For Endpoint name, get in an [endpoint](https://jobs.ahaconsultant.co.in) name (between 1-50 alphanumeric characters). |
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5. For Variety of instances, go into a variety of (in between 1-100). |
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6. For example type, select your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. |
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Optionally, you can configure sophisticated security and infrastructure settings, including virtual [personal](https://www.sociopost.co.uk) cloud (VPC) networking, service function approvals, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might want to evaluate these [settings](http://121.40.234.1308899) to line up with your [organization's security](https://jobs.web4y.online) and compliance requirements. |
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7. Choose Deploy to begin using the model.<br> |
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<br>When the release is total, you can test 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 interface where you can experiment with various triggers and adjust design parameters 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 optimum results. For example, material for inference.<br> |
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<br>This is an exceptional way to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The play ground offers immediate feedback, assisting you comprehend how the design reacts to numerous inputs and letting you tweak your triggers for optimal results.<br> |
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<br>You can rapidly check the design in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need 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 demonstrates how to carry out inference 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://apkjobs.com) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, 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, built-in algorithms, and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:MonserrateHuntin) prebuilt ML options that you can [release](https://hr-2b.su) with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](http://121.37.166.03000) models to your use case, with your information, and release them into production utilizing either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 model through [SageMaker JumpStart](http://stockzero.net) provides two hassle-free approaches: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the technique that finest fits 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](http://47.98.226.2403000) to deploy DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:Joann98S268) pick 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 JumpStart in the navigation pane.<br> |
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<br>The model internet browser shows available models, with details like the supplier name and model abilities.<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 key details, including:<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 appropriate), [suggesting](https://baescout.com) that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design<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 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 crucial details, such as:<br> |
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<br>- Model description. |
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- License details. |
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[- Technical](http://valueadd.kr) requirements. |
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- Usage guidelines<br> |
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<br>Before you deploy the design, it's advised to evaluate the design details and license terms to confirm 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, use the instantly generated name or create a custom one. |
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8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, go into the variety of instances (default: 1). |
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Selecting suitable instance types and counts is vital for expense and efficiency optimization. Monitor [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:MarcR997450156) your deployment to change these settings as needed.Under Inference type, [Real-time inference](http://152.136.126.2523000) 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 design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. |
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11. Choose Deploy to release the model.<br> |
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<br>The deployment procedure can take a number of minutes to complete.<br> |
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<br>When release is complete, your endpoint status will change to InService. At this point, the model is all set to accept reasoning demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can conjure up the model using a SageMaker runtime customer and incorporate it with your applications.<br> |
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<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br> |
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<br>To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and [utilize](https://24cyber.ru) DeepSeek-R1 for [inference programmatically](https://aladin.tube). The code for [releasing](http://63.141.251.154) the design is provided in the Github here. You can clone the note pad and run from SageMaker Studio.<br> |
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<br>You can run extra demands against the predictor:<br> |
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<br>Implement guardrails and run inference with your [SageMaker JumpStart](https://jobsthe24.com) 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 create a [guardrail](https://gitea.oo.co.rs) using the Amazon Bedrock console 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 prevent unwanted charges, complete 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 deployed the design utilizing Amazon Bedrock Marketplace, complete the following actions:<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, find the endpoint you want to erase. |
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3. Select the endpoint, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) and on the Actions menu, select Delete. |
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4. Verify the endpoint details to make certain you're deleting the proper deployment: 1. [Endpoint](http://47.93.56.668080) name. |
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2. Model name. |
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3. Endpoint status<br> |
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<br>Delete the SageMaker JumpStart predictor<br> |
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<br>The SageMaker JumpStart design you deployed will sustain costs if you leave it running. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints 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 release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](https://myclassictv.com) designs, SageMaker JumpStart pretrained designs, Amazon SageMaker [JumpStart](https://gitea.malloc.hackerbots.net) 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](https://iuridictum.pecina.cz) for [Inference](https://git.wheeparam.com) at AWS. He helps emerging generative [AI](http://supervipshop.net) business develop innovative options using AWS services and sped up compute. Currently, he is concentrated on developing methods for fine-tuning and optimizing the inference performance of large language designs. In his leisure time, [Vivek delights](https://www.canaddatv.com) in treking, seeing motion pictures, and trying various cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.pilzinsel64.de) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://yes.youkandoit.com) 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 working on generative [AI](http://xn--9t4b21gtvab0p69c.com) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://aladin.tube) hub. She is passionate about developing options that assist customers accelerate their [AI](https://score808.us) journey and unlock organization worth.<br> |
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