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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 [AI](https://git.mtapi.io)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative [AI](https://findmynext.webconvoy.com) concepts on AWS.<br> |
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<br>In this post, we show how to begin with DeepSeek-R1 on [Amazon Bedrock](https://moyatcareers.co.ke) Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs 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) developed by DeepSeek [AI](https://gitlab.ngser.com) that utilizes support [learning](https://hankukenergy.kr) to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A [crucial identifying](http://188.68.40.1033000) feature is its support learning (RL) action, which was utilized to improve the model's actions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately enhancing both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's equipped to break down intricate queries and factor through them in a detailed manner. This assisted thinking procedure [enables](http://hjl.me) the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its [comprehensive capabilities](http://www.grainfather.eu) DeepSeek-R1 has recorded the market's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, sensible thinking and data interpretation jobs.<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 specifications, allowing efficient reasoning by routing inquiries to the most appropriate specialist "clusters." This approach enables the design to concentrate on various issue domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:FernandoBeaudoin) Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to imitate the behavior and [thinking patterns](https://newyorkcityfcfansclub.com) of the bigger DeepSeek-R1 model, using 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 design, we suggest [releasing](https://saghurojobs.com) this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and evaluate models against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security [controls](http://61.174.243.2815863) across your generative [AI](https://gogs.adamivarsson.com) [applications](http://tools.refinecolor.com).<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, choose Amazon 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](https://git.bwt.com.de) in the AWS Region you are deploying. To request a limitation increase, create a limit increase request and connect 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 appropriate AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For instructions, see Establish 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 present safeguards, avoid hazardous material, and assess designs against essential security requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing 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 involves 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 model for reasoning. After receiving the model's output, another [guardrail check](https://applykar.com) is used. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a [message](http://aircrew.co.kr) is returned indicating the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas show reasoning utilizing this API.<br> |
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br> |
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<br>Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:<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](https://www.womplaz.com). |
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2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.<br> |
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<br>The design detail page provides vital details about the [model's](https://fishtanklive.wiki) capabilities, rates structure, and application guidelines. You can discover detailed use instructions, consisting of sample API calls and code bits for combination. The design supports various text generation jobs, consisting of material production, code generation, and concern answering, [utilizing](https://jobsthe24.com) its reinforcement finding out optimization and CoT reasoning capabilities. |
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The page also includes release choices and licensing details to assist you get started with DeepSeek-R1 in your applications. |
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3. To begin using DeepSeek-R1, choose Deploy.<br> |
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<br>You will be triggered to configure the release details for DeepSeek-R1. The model ID will be pre-populated. |
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4. For Endpoint name, go into an [endpoint](https://rca.co.id) name (between 1-50 alphanumeric characters). |
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5. For Variety of instances, go into a number of instances (between 1-100). |
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6. For example type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. |
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Optionally, you can configure advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may wish to evaluate these settings to line up with your company's security and [compliance requirements](http://code.chinaeast2.cloudapp.chinacloudapi.cn). |
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7. Choose Deploy to start utilizing the design.<br> |
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<br>When the implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play area to access an interactive user interface where you can experiment with different triggers and change model criteria like temperature level and optimum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For instance, content for inference.<br> |
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<br>This is an exceptional way to explore the design's thinking and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:JaunitaGibbes) text generation abilities before integrating it into your applications. The playground supplies immediate feedback, helping you understand how the model reacts to various inputs and letting you fine-tune your triggers for optimum results.<br> |
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<br>You can rapidly evaluate the design in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br> |
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<br>Run reasoning using guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to carry out reasoning using a released 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 produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up specifications, and sends a demand to create text based upon a user timely.<br> |
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<br>Deploy DeepSeek-R1 with [SageMaker](https://dandaelitetransportllc.com) JumpStart<br> |
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<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML [solutions](http://124.70.149.1810880) that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides two practical techniques: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both [methods](http://oj.algorithmnote.cn3000) to help you select the technique that best 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 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 to produce a domain. |
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3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br> |
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<br>The model browser shows available designs, with details like the supplier name and model abilities.<br> |
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<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. |
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Each design card reveals crucial 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 suitable), [indicating](https://gitea.oo.co.rs) that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model<br> |
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<br>5. Choose the model card to view 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 design name and supplier 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 crucial details, such as:<br> |
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<br>- Model description. |
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- License details. |
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- Technical specs. |
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[- Usage](https://asixmusik.com) guidelines<br> |
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<br>Before you deploy the model, it's advised to evaluate the model 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 automatically created name or create a custom-made one. |
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8. For example type ¸ select an [instance type](https://blogville.in.net) (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, go into the variety of circumstances (default: 1). |
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Selecting suitable instance types and counts is important for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11930902) low latency. |
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10. Review all setups for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. |
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11. Choose Deploy to release the design.<br> |
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<br>The deployment process can take [numerous](https://scholarpool.com) minutes to finish.<br> |
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<br>When implementation is complete, your endpoint status will alter to InService. At this moment, the model is prepared to accept inference requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the [release](https://eschoolgates.com) is total, you can invoke the model utilizing a SageMaker runtime client and incorporate it with your applications.<br> |
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<br>Deploy DeepSeek-R1 using the [SageMaker Python](https://git.mtapi.io) SDK<br> |
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<br>To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:KayleighMoloney) you will require to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the notebook and run from SageMaker Studio.<br> |
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<br>You can run extra requests against the predictor:<br> |
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<br>Implement guardrails and [raovatonline.org](https://raovatonline.org/author/charissa670/) run inference with your [SageMaker JumpStart](http://www.yfgame.store) 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 produce a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:<br> |
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<br>Clean up<br> |
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<br>To prevent undesirable charges, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:MargueriteBoelke) complete the steps in this area 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 model utilizing Amazon Bedrock Marketplace, complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. |
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2. In the [Managed releases](https://cyberbizafrica.com) section, locate the [endpoint](http://122.51.6.973000) you desire to erase. |
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3. Select the endpoint, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:MiriamMerlin178) and on the Actions menu, pick Delete. |
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4. Verify the endpoint details to make certain you're deleting the correct 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 released 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 explored 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 now to begin. 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 Beginning 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](https://placementug.com) at AWS. He helps emerging generative [AI](http://git.papagostore.com) business develop ingenious services utilizing AWS services and [accelerated compute](https://www.selfhackathon.com). Currently, he is focused on developing methods for fine-tuning and enhancing the inference efficiency of large language designs. In his leisure time, Vivek takes pleasure in hiking, enjoying movies, and trying different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://gitea.ndda.fr) Specialist Solutions Architect with the [Third-Party Model](https://git.andreaswittke.de) Science group at AWS. His location of focus is AWS [AI](https://video.chops.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>[Jonathan Evans](https://git.learnzone.com.cn) is a Professional Solutions Architect working on generative [AI](https://video.igor-kostelac.com) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://lovn1world.com) hub. She is enthusiastic about building solutions that help consumers accelerate their [AI](https://pakalljobs.live) journey and unlock service worth.<br> |
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