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<br>Today, we are excited 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](https://reeltalent.gr) DeepSeek [AI](https://manpoweradvisors.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](https://www.joinyfy.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 actions to release the distilled variations of the models as well.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://git.juxiong.net) that utilizes support finding out to improve reasoning capabilities through a multi-stage training [procedure](https://grailinsurance.co.ke) from a DeepSeek-V3-Base structure. An essential distinguishing function is its reinforcement knowing (RL) step, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:LashayAlderson9) which was used to fine-tune the model's responses beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, [suggesting](http://hoenking.cn3000) it's geared up to break down intricate questions and reason through them in a detailed manner. This assisted thinking procedure permits the model to produce more accurate, transparent, and detailed responses. This design [integrates RL-based](http://eliment.kr) fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has caught the market's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, sensible [thinking](https://weworkworldwide.com) and data interpretation jobs.<br> |
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, allowing efficient [inference](http://59.57.4.663000) by routing queries to the most relevant professional "clusters." This technique allows the design to specialize in different issue domains while maintaining overall efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective models to simulate the habits and reasoning 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 recommend releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to [introduce](https://git.home.lubui.com8443) safeguards, prevent damaging material, and [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:RaulThorson) assess models against key security criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](http://www.chemimart.kr) applications.<br> |
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<br>Prerequisites<br> |
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<br>To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for [endpoint usage](https://weworkworldwide.com). Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limitation boost, develop a limit boost request and [links.gtanet.com.br](https://links.gtanet.com.br/terilenz4996) reach out to your account team.<br> |
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<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS [Identity](https://opedge.com) and Gain Access To Management (IAM) permissions to utilize Amazon . For directions, see Set up approvals to utilize guardrails for material 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 damaging material, and assess designs against key security requirements. You can execute security [procedures](https://sadegitweb.pegasus.com.mx) for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model reactions released 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 general flow involves the following steps: First, the system receives an input for the design. 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 is used. If the output passes this final check, it's returned as the final 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 happened at the input or output stage. The examples showcased in the following sections 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 gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To [gain access](https://git.cyu.fr) to DeepSeek-R1 in Amazon Bedrock, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, select Model catalog 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 invoke the design. It doesn't support Converse APIs and other Amazon Bedrock [tooling](https://www.loupanvideos.com). |
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2. Filter for DeepSeek as a [company](https://earthdailyagro.com) and choose the DeepSeek-R1 design.<br> |
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<br>The design detail page provides necessary details about the design's abilities, prices structure, and application guidelines. You can find detailed usage directions, including sample API calls and code bits for combination. The model supports numerous text generation tasks, including material production, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities. |
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The page also consists of implementation alternatives and licensing details to help you start 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 [triggered](http://park8.wakwak.com) to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. |
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4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Number of circumstances, go into a number of circumstances (in between 1-100). |
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6. For example type, select your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. |
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Optionally, you can configure advanced security and facilities settings, including virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may want to review these settings to align with your company'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 deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. |
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8. Choose Open in play area to access an interactive user interface where you can experiment with various triggers and adjust design criteria like temperature and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, material for inference.<br> |
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<br>This is an exceptional way to check out the design's thinking and text generation capabilities before incorporating it into your applications. The playground provides instant feedback, assisting you comprehend how the model reacts to various inputs and letting you [fine-tune](http://jenkins.stormindgames.com) your triggers for optimal outcomes.<br> |
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<br>You can [rapidly test](https://goalsshow.com) the model in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br> |
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<br>Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a demand to produce text based upon a user 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) center with FMs, built-in algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient methods: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you select the method that finest 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 deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, choose 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, choose JumpStart in the navigation pane.<br> |
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<br>The design browser displays available designs, with details like the company name and design capabilities.<br> |
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. |
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Each model card shows essential details, including:<br> |
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<br>- Model name |
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- Provider name |
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- Task classification (for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:Myron03850) example, Text Generation). |
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Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon [Bedrock](https://croart.net) APIs to invoke 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 design details page includes the following details:<br> |
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<br>- The design name and company details. |
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Deploy button to release the design. |
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About and Notebooks tabs with [detailed](http://47.108.94.35) 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 specs. |
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- Usage standards<br> |
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<br>Before you deploy the design, it's suggested to evaluate the design details and license terms to verify compatibility with your use 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 generated name or produce a custom-made one. |
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8. For [Instance type](https://apk.tw) ¸ select a circumstances type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, enter the number of circumstances (default: 1). |
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Selecting appropriate [circumstances types](https://fondnauk.ru) and counts is crucial for cost and [performance optimization](http://football.aobtravel.se). Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. |
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10. Review all setups for accuracy. For this design, we strongly advise adhering 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 deployment process can take numerous minutes to finish.<br> |
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<br>When implementation is total, your endpoint status will alter to [InService](https://www.facetwig.com). At this moment, the model is all set to accept reasoning demands through the [endpoint](http://git.cnibsp.com). You can keep track of the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment 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 start with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and [links.gtanet.com.br](https://links.gtanet.com.br/zarakda51931) range 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 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 develop a guardrail utilizing the Amazon Bedrock console or [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:RosieG65174) the API, and implement it as revealed in the following code:<br> |
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<br>Clean up<br> |
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<br>To avoid 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 models in the navigation pane, choose Marketplace deployments. |
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2. In the Managed deployments area, locate the [endpoint](https://www.diekassa.at) you desire to erase. |
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3. Select the endpoint, and on the Actions menu, choose 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 model you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want 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](https://semtleware.com) JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with [Amazon SageMaker](https://gitea.v-box.cn) JumpStart models, SageMaker JumpStart pretrained models, 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](https://activitypub.software) Architect for Inference at AWS. He assists emerging generative [AI](https://vlabs.synology.me:45) business construct ingenious solutions using AWS services and accelerated calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the inference efficiency of large [language](https://karmadishoom.com) models. In his downtime, Vivek enjoys hiking, enjoying movies, and trying various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://joydil.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://xinh.pro.vn) [accelerators](https://tikness.com) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br> |
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<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](http://www.asystechnik.com) with the Third-Party Model [Science](https://www.joinyfy.com) 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 [ratemywifey.com](https://ratemywifey.com/author/felishawatk/) generative [AI](https://career.ltu.bg) hub. She is passionate about developing services that help customers accelerate their [AI](http://suvenir51.ru) journey and unlock company worth.<br> |
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