Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://littlebigempire.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](https://chaakri.com) 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. You can follow comparable actions to deploy the distilled versions of the designs as well.<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://2t-s.com) that uses reinforcement learning to boost thinking capabilities through a [multi-stage training](https://git.rell.ru) procedure from a DeepSeek-V3-Base structure. An essential differentiating feature is its support learning (RL) step, which was used to refine the design's responses beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's equipped to break down [complicated questions](https://www.pkjobshub.store) and reason through them in a detailed manner. This guided thinking process allows the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation model that can be integrated into various workflows such as representatives, sensible reasoning and information interpretation tasks.<br> |
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient reasoning by routing queries to the most relevant expert "clusters." This [approach](https://teachinthailand.org) allows the design to focus on various issue domains while maintaining overall [performance](https://git.arcbjorn.com). DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, [wiki-tb-service.com](http://wiki-tb-service.com/index.php?title=Benutzer:Milla01Z3855169) we will use an ml.p5e.48 xlarge instance to deploy the design. 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 abilities](http://120.201.125.1403000) of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient models to simulate the habits and [thinking patterns](https://selfloveaffirmations.net) of the larger DeepSeek-R1 model, utilizing it as a teacher design.<br> |
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<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and evaluate models against key safety criteria. At the time of composing this blog site, for [disgaeawiki.info](https://disgaeawiki.info/index.php/User:MillieTum68) DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://intermilanfansclub.com) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, [choose Amazon](https://jobedges.com) SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limit increase, create a limit 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) approvals to use Amazon Bedrock Guardrails. For directions, see Set up authorizations 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, avoid hazardous content, and assess models against key security requirements. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and design 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 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 out to the model for inference. After receiving 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 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 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, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, select Model brochure under Foundation designs 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 APIs and other [Amazon Bedrock](https://gitlab.rail-holding.lt) tooling. |
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2. Filter for [DeepSeek](http://www.iilii.co.kr) as a service provider and pick the DeepSeek-R1 design.<br> |
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<br>The model detail page supplies vital details about the model's capabilities, pricing structure, and execution guidelines. You can discover detailed usage directions, consisting of sample API calls and code snippets for integration. The model supports numerous text generation jobs, consisting of material production, code generation, and [ratemywifey.com](https://ratemywifey.com/author/xjstrudi716/) concern answering, utilizing its support discovering optimization and CoT reasoning abilities. |
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The page likewise includes deployment choices and licensing [details](https://git.intellect-labs.com) to assist you start with DeepSeek-R1 in your applications. |
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3. To start utilizing DeepSeek-R1, pick Deploy.<br> |
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<br>You will be prompted to set up the deployment details for DeepSeek-R1. The model 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 Variety of circumstances, enter a number of circumstances (between 1-100). |
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6. For example type, select your [circumstances type](https://dreamtvhd.com). For ideal efficiency 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 [private cloud](https://pleroma.cnuc.nu) (VPC) networking, service function approvals, and [encryption](https://abadeez.com) settings. For most utilize cases, the default settings will work well. However, for production deployments, you might want to review these settings to align with your organization's security and compliance requirements. |
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7. Choose Deploy to start utilizing the design.<br> |
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<br>When the deployment 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 ground to access an interactive interface where you can try out different prompts and adjust model specifications like temperature level and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, material for inference.<br> |
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<br>This is an outstanding way to check out the design's reasoning and text generation abilities before integrating it into your applications. The play ground supplies immediate feedback, assisting you comprehend how the model reacts to numerous inputs and letting you tweak your triggers for optimum results.<br> |
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<br>You can quickly test the design in the play ground through the UI. However, to conjure up the deployed model [programmatically](https://www.bisshogram.com) with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br> |
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<br>Run inference utilizing guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform reasoning using a deployed DeepSeek-R1 model 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 create the guardrail, see the GitHub repo. After you have [developed](https://www.finceptives.com) the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up [reasoning](http://124.223.100.383000) parameters, and sends out a request to create [text based](http://114.111.0.1043000) 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](http://www.homeserver.org.cn3000) (ML) hub with FMs, built-in algorithms, and prebuilt ML [services](https://git.wun.im) that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 model through SageMaker JumpStart offers two practical approaches: utilizing the [user-friendly SageMaker](https://www.freetenders.co.za) JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the approach that best matches your needs.<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 using 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 to create a domain. |
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3. On the SageMaker Studio console, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:Juliane3350) select JumpStart in the navigation pane.<br> |
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<br>The model browser shows available models, with details like the [supplier](https://gitlab.amatasys.jp) name and model abilities.<br> |
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<br>4. Look for DeepSeek-R1 to view 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 category (for instance, Text Generation). |
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Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, allowing you to use [Amazon Bedrock](https://www.arztstellen.com) APIs to conjure up the design<br> |
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<br>5. Choose the model card to see 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 model name and supplier details. |
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Deploy button to deploy the model. |
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About and Notebooks tabs with [detailed](http://git.anyh5.com) details<br> |
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<br>The About tab includes essential details, such as:<br> |
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<br>- Model description. |
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- License details. |
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- Technical requirements. |
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- Usage standards<br> |
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<br>Before you release the design, it's suggested to examine the design details and license terms to validate compatibility with your usage case.<br> |
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<br>6. Choose Deploy to proceed with deployment.<br> |
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<br>7. For Endpoint name, use the automatically produced name or create a customized one. |
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8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, get in the number of [instances](https://thankguard.com) (default: 1). |
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Selecting proper instance types and counts is important for expense and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. |
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10. Review all setups for precision. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. |
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11. Choose Deploy to deploy the model.<br> |
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<br>The implementation process can take a number of minutes to finish.<br> |
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<br>When deployment is complete, your endpoint status will alter to InService. At this point, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:NevilleWitcher0) the design is all set to accept reasoning demands through the [endpoint](http://www.homeserver.org.cn3000). You can keep track of the implementation progress on the [SageMaker](https://gertsyhr.com) console Endpoints page, which will show pertinent metrics and status details. When the [implementation](https://www.sewosoft.de) is complete, you can invoke the model using a SageMaker runtime client 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 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that [demonstrates](http://git.keliuyun.com55676) how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and run from [SageMaker Studio](https://git.alexhill.org).<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 predictor<br> |
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<br>Similar to Amazon Bedrock, you can also utilize 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 revealed in the following code:<br> |
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<br>Tidy up<br> |
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<br>To prevent unwanted charges, complete the actions in this section to clean 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 design utilizing Amazon Bedrock Marketplace, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. |
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2. In the [Managed releases](https://scode.unisza.edu.my) section, find the [endpoint](https://www.onlywam.tv) you wish 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 name. |
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2. Model name. |
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3. Endpoint status<br> |
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<br>Delete the SageMaker JumpStart predictor<br> |
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<br>The SageMaker JumpStart model you released will sustain costs if you leave it [running](https://district-jobs.com). Use the following code to delete the [endpoint](https://www.assistantcareer.com) if you desire 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to 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 Architect for Inference at AWS. He helps emerging generative [AI](https://seedvertexnetwork.co.ke) business construct solutions utilizing AWS [services](https://ozgurtasdemir.net) and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the inference performance of large language models. In his spare time, Vivek takes pleasure in hiking, watching movies, and attempting various cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://lyo.kr) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His [location](https://kennetjobs.com) of focus is AWS [AI](http://47.113.115.239:3000) 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 dealing with generative [AI](https://elmerbits.com) with the Third-Party Model Science team 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://git-dev.xyue.zip:8443) hub. She is passionate about developing solutions that assist consumers accelerate their [AI](https://www.ayurjobs.net) journey and unlock organization value.<br> |
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