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 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 [AI](https://pk.thehrlink.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative [AI](https://socipops.com) concepts 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. You can follow comparable 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 large language model (LLM) established by DeepSeek [AI](http://123.56.193.182:3000) that utilizes support learning to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating feature is its reinforcement knowing (RL) action, which was utilized to improve the design's actions beyond the basic pre-training and tweak procedure. By incorporating RL, [wiki.whenparked.com](https://wiki.whenparked.com/User:JamilaWymark120) DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's geared up to break down intricate inquiries and factor through them in a detailed manner. This guided reasoning process enables the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, logical thinking and data interpretation jobs.<br>
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<br>DeepSeek-R1 [utilizes](http://szfinest.com6060) a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, allowing effective reasoning by routing queries to the most appropriate specialist "clusters." This technique enables the model to focus on different problem domains while maintaining total performance. 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 deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of [training](https://ehrsgroup.com) smaller, more effective designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor 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 advise releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and assess models against [crucial](https://gitea-working.testrail-staging.com) safety requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](http://g-friend.co.kr) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 design, you [require access](https://gomyneed.com) to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge [circumstances](https://gitr.pro) in the AWS Region you are deploying. To request a limitation boost, create a limitation boost demand and [connect](http://tools.refinecolor.com) to your account team.<br>
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<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, [surgiteams.com](https://surgiteams.com/index.php/User:MauraDasilva2) see Set up 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 allows you to present safeguards, avoid hazardous material, and examine designs against essential safety [requirements](https://tenacrebooks.com). You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.<br>
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<br>The basic circulation includes the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for inference. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas demonstrate 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 offers you access to over 100 popular, emerging, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:NoemiStrack042) and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br>
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<br>1. On the Amazon Bedrock console, select Model catalog under Foundation models in the [navigation](https://sing.ibible.hk) pane.
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At the time of writing this post, you can use the InvokeModel API 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 supplier and select the DeepSeek-R1 model.<br>
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<br>The model detail page offers vital details about the design's abilities, rates structure, and application guidelines. You can find detailed usage instructions, including calls and code bits for combination. The design supports numerous text generation jobs, consisting of material production, code generation, and question answering, using its support learning optimization and CoT thinking abilities.
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The page also consists of release choices and licensing details to help you get going with DeepSeek-R1 in your applications.
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3. To start utilizing DeepSeek-R1, choose Deploy.<br>
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<br>You will be prompted to configure the [deployment details](https://www.laciotatentreprendre.fr) 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 circumstances, go into a number of circumstances (between 1-100).
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6. For Instance type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
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Optionally, you can configure sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might desire to examine these settings to align with your organization's security 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 complete, you can check 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 try out various prompts and adjust model parameters like temperature level and maximum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, content for reasoning.<br>
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<br>This is an outstanding way to check out the model's thinking and text generation capabilities before integrating it into your applications. The play ground offers instant feedback, helping you understand how the model reacts to various inputs and letting you tweak your prompts for optimum results.<br>
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<br>You can rapidly evaluate the model in the play area through the UI. However, to invoke the deployed 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 shows how to perform inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:FidelChester649) utilize the following code to implement guardrails. The script initializes the bedrock_[runtime](https://www.diekassa.at) client, [configures inference](https://www.pkgovtjobz.site) specifications, and sends a request to produce text based upon 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 models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 practical approaches: using the [intuitive SageMaker](https://devfarm.it) JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the [approach](https://gitea.mpc-web.jp) that best 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 actions to release DeepSeek-R1 using 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](http://www.gz-jj.com) users will be [prompted](http://18.178.52.993000) to develop a domain.
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3. On the SageMaker Studio console, choose [JumpStart](https://grailinsurance.co.ke) in the navigation pane.<br>
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<br>The design internet browser shows available models, with [details](https://arbeitswerk-premium.de) like the service provider name and [model capabilities](https://cheere.org).<br>
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
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Each design card shows key details, including:<br>
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<br>- Model name
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- Provider name
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- Task category (for instance, Text Generation).
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Bedrock Ready badge (if suitable), suggesting that this design can be signed up with Amazon Bedrock, enabling you to use [Amazon Bedrock](http://private.flyautomation.net82) APIs to invoke the design<br>
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<br>5. Choose the [model card](http://13.228.87.95) to see the [model details](https://truthbook.social) page.<br>
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<br>The model details page includes 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](https://gitea.egyweb.se) 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](https://www.keeloke.com) specs.
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- Usage guidelines<br>
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<br>Before you release the model, it's suggested to review the model details and license terms to verify compatibility with your usage case.<br>
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<br>6. Choose Deploy to continue with deployment.<br>
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<br>7. For Endpoint name, utilize the automatically generated name or [produce](https://git.pawott.de) a custom-made one.
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8. For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial instance count, enter the [variety](https://losangelesgalaxyfansclub.com) of circumstances (default: 1).
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Selecting appropriate instance types and counts is important for cost and efficiency optimization. Monitor your implementation to change these [settings](https://git.tasu.ventures) as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and [low latency](https://gitea.phywyj.dynv6.net).
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10. Review all setups for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
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11. Choose Deploy to deploy the model.<br>
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<br>The [release process](http://119.130.113.2453000) can take a number of minutes to finish.<br>
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<br>When deployment is total, your endpoint status will alter to InService. At this moment, the design is all set to accept reasoning requests through the endpoint. You can monitor [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:CandidaKahl1) the implementation development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is total, 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 get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS permissions and [gratisafhalen.be](https://gratisafhalen.be/author/caryencarna/) environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for [deploying](https://profesional.id) the model is supplied in the Github here. You can clone the [notebook](https://www.jr-it-services.de3000) and range from SageMaker Studio.<br>
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<br>You can run additional requests against the predictor:<br>
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<br>[Implement guardrails](http://39.99.134.1658123) 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 using the Amazon Bedrock console or the API, and execute it as displayed 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 section 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 model using Amazon Bedrock Marketplace, total the following actions:<br>
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases.
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2. In the Managed releases area, locate the endpoint you want to delete.
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3. Select the endpoint, and on the Actions menu, pick Delete.
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4. Verify the endpoint details to make certain you're erasing the appropriate release: 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 design you deployed will sustain expenses 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 checked out how you can access and [release](https://gitea.mpc-web.jp) the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.<br>
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<br>About the Authors<br>
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<br>Vivek Gangasani is a Lead Specialist [Solutions](https://netgork.com) Architect for Inference at AWS. He assists emerging generative [AI](https://gitlab-dev.yzone01.com) business construct ingenious [solutions utilizing](http://47.93.156.1927006) AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and [optimizing](https://git.magicvoidpointers.com) the inference performance of big language designs. In his downtime, Vivek enjoys treking, enjoying movies, and trying different cuisines.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://ecoreal.kr) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://106.14.140.71:3000) 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://sundaycareers.com) with the Third-Party Model Science team at AWS.<br>
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<br>Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.itbcode.com) hub. She is [enthusiastic](http://1.94.30.13000) about developing solutions that help consumers accelerate their [AI](http://154.64.253.77:3000) journey and unlock company worth.<br>
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