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 [thrilled](https://aravis.dev) 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://upleta.rackons.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://geetgram.com) ideas on AWS.<br>
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<br>In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock [Marketplace](http://62.234.223.2383000) and SageMaker JumpStart. You can follow comparable [actions](http://git.scraperwall.com) 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 large language model (LLM) established by DeepSeek [AI](http://git.acdts.top:3000) that utilizes reinforcement learning to improve reasoning [capabilities](https://posthaos.ru) through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential identifying feature is its support knowing (RL) action, which was utilized to fine-tune the design's responses beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, [meaning](https://47.100.42.7510443) it's geared up to break down intricate queries and factor through them in a detailed way. This guided thinking process enables the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, rational reasoning and information interpretation tasks.<br>
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The [MoE architecture](https://gitea.phywyj.dynv6.net) allows activation of 37 billion criteria, making it possible for efficient reasoning by routing questions to the most pertinent professional "clusters." This approach permits the design to focus on different problem domains while maintaining overall performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective models to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 model, [utilizing](https://www.contraband.ch) it as an instructor model.<br>
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<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or [Bedrock](http://123.56.193.1823000) Marketplace. Because DeepSeek-R1 is an emerging design, [oeclub.org](https://oeclub.org/index.php/User:MilanNickson63) we recommend deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1093372) assess models against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://job.da-terascibers.id) applications.<br>
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<br>Prerequisites<br>
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<br>To deploy the DeepSeek-R1 design, 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, select Amazon SageMaker, and verify you're using 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 releasing. To request a limitation increase, produce a [limit boost](https://www.bakicicepte.com) request and reach out 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 permissions to use guardrails for [pediascape.science](https://pediascape.science/wiki/User:EpifaniaStonehou) 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 present safeguards, prevent damaging material, and assess designs against key safety requirements. You can execute security measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.<br>
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<br>The basic circulation includes the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for [inference](http://git.agentum.beget.tech). After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. 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 occurred at the input or output stage. The examples showcased in the following sections show inference using this API.<br>
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
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<br>Amazon Bedrock Marketplace provides 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 steps:<br>
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<br>1. On the Amazon Bedrock console, choose Model brochure 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 up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.<br>
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<br>The design detail page offers important details about the model's capabilities, prices structure, and execution guidelines. You can find [detailed](https://gitea.phywyj.dynv6.net) usage directions, consisting of sample API calls and code bits for combination. The model supports different text generation jobs, [including material](https://www.keeloke.com) production, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning capabilities.
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The page likewise consists of implementation choices and licensing [details](https://157.56.180.169) 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 release details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
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5. For Number of circumstances, go into a number of instances (in between 1-100).
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6. For Instance type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
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Optionally, you can configure innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For a lot of use cases, the default settings will work well. However, for [production](https://wheeoo.com) deployments, you may desire to evaluate these settings to line up with your organization's security and compliance requirements.
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7. Choose Deploy to begin utilizing the model.<br>
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<br>When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
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8. Choose Open in play ground to access an interactive user interface where you can experiment with various prompts and change design criteria like temperature and optimum length.
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, content for reasoning.<br>
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<br>This is an exceptional way to check out the design's thinking and [text generation](http://git.agentum.beget.tech) abilities before incorporating it into your applications. The playground provides immediate feedback, assisting you understand how the [model reacts](https://www.lshserver.com3000) to different inputs and letting you tweak your triggers for optimum outcomes.<br>
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<br>You can rapidly evaluate the design in the play area through the UI. However, to conjure up 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](https://git.dadunode.com) with the released DeepSeek-R1 endpoint<br>
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<br>The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have produced the guardrail, [utilize](http://bolling-afb.rackons.com) the following code to [implement guardrails](https://www.boatcareer.com). The script initializes the bedrock_[runtime](http://58.87.67.12420080) customer, [configures reasoning](https://teachinthailand.org) parameters, and sends a demand to produce 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 release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production utilizing either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker [Python SDK](https://apyarx.com). Let's explore both techniques to assist you select the method that finest matches your needs.<br>
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<br>Deploy DeepSeek-R1 through [SageMaker JumpStart](https://cvmira.com) UI<br>
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<br>Complete the following steps to deploy 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 [prompted](https://daeshintravel.com) to produce a domain.
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3. On the SageMaker Studio console, choose JumpStart in the [navigation](https://copyright-demand-letter.com) pane.<br>
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<br>The model web browser shows available designs, with details like the provider name and [design abilities](https://wiki.armello.com).<br>
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
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Each design card reveals crucial details, consisting of:<br>
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<br>[- Model](https://thesecurityexchange.com) 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 applicable), indicating that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design<br>
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<br>5. Choose the model card to view the model 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 provider details.
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Deploy button to deploy the design.
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About and Notebooks tabs with detailed details<br>
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<br>The About tab includes important details, such as:<br>
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<br>- Model description.
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- License details.
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- Technical requirements.
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- Usage standards<br>
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<br>Before you release the model, it's advised to evaluate the model details and license terms to confirm compatibility with your use case.<br>
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<br>6. Choose Deploy to [continue](http://124.222.85.1393000) with deployment.<br>
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<br>7. For Endpoint name, use the instantly generated name or create a customized one.
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8. For Instance type ¸ select a circumstances type (default: [gratisafhalen.be](https://gratisafhalen.be/author/vernitasett/) ml.p5e.48 xlarge).
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9. For Initial instance count, get in the number of circumstances (default: 1).
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Selecting suitable instance types and counts is essential for cost and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
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10. Review all setups for accuracy. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
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11. [Choose Deploy](https://origintraffic.com) to deploy the design.<br>
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<br>The implementation procedure can take a number of minutes to finish.<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 inference demands through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will [display pertinent](https://welcometohaiti.com) metrics and status details. When the deployment is total, you can conjure up the design using 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 going with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is [supplied](https://git.revoltsoft.ru) in the Github here. You can clone the notebook 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the [Amazon Bedrock](https://www.bluedom.fr) console or the API, and implement it as shown in the following code:<br>
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<br>Clean up<br>
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<br>To prevent unwanted charges, complete the actions in this area to clean 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 design using 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, [choose Marketplace](http://www.boot-gebraucht.de) implementations.
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2. In the Managed implementations area, find the endpoint you wish to delete.
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3. Select the endpoint, and on the Actions menu, [pick Delete](http://63.141.251.154).
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4. Verify the [endpoint details](https://www.mapsisa.org) to make certain you're deleting the correct deployment: 1. Endpoint name.
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2. Model name.
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3. Endpoint status<br>
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<br>Delete the SageMaker JumpStart predictor<br>
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<br>The [SageMaker JumpStart](https://dimans.mx) model you deployed will sustain expenses if you leave it running. Use the following code to erase 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 [checked](https://src.strelnikov.xyz) out how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon [Bedrock Marketplace](https://mhealth-consulting.eu) now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.<br>
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<br>About the Authors<br>
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<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://www.joinyfy.com) companies build ingenious options utilizing AWS services and accelerated calculate. Currently, he is focused on establishing methods for fine-tuning and enhancing the inference performance of large language designs. In his leisure time, Vivek enjoys hiking, seeing movies, and attempting different cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://hlatube.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://daeasecurity.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
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<br>Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://phdjobday.eu) with the Third-Party Model [Science](https://src.strelnikov.xyz) group at AWS.<br>
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<br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitlab.tncet.com) center. She is enthusiastic about constructing solutions that assist customers accelerate their [AI](http://47.108.182.66:7777) journey and unlock business worth.<br>
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