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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 DeepSeek [AI](https://professionpartners.co.uk)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled [variations ranging](http://82.146.58.193) from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your [generative](https://marcosdumay.com) [AI](https://thecodelab.online) ideas on AWS.<br>
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<br>In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the designs also.<br>
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<br>Overview of DeepSeek-R1<br>
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<br>DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://thecodelab.online) that utilizes reinforcement learning to enhance thinking abilities through a [multi-stage training](http://121.36.37.7015501) procedure from a DeepSeek-V3-Base structure. A key differentiating function is its support learning (RL) step, which was used to refine the model's responses beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, eventually enhancing both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's geared up to break down intricate queries and factor through them in a detailed manner. This [assisted reasoning](https://noinai.com) process permits the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation model that can be integrated into various workflows such as agents, sensible thinking and information analysis tasks.<br>
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, allowing efficient inference by routing questions to the most relevant expert "clusters." This approach allows the model to concentrate on different problem domains while maintaining total efficiency. DeepSeek-R1 requires a minimum of 800 GB of [HBM memory](http://expertsay.blog) in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient models to imitate the behavior and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor design.<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 place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and evaluate models against essential safety criteria. At the time of writing this blog site, [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:LeonoraGresham4) for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](https://www.xtrareal.tv) [applications](http://repo.z1.mastarjeta.net).<br>
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<br>Prerequisites<br>
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<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for [mediawiki.hcah.in](https://mediawiki.hcah.in/index.php?title=User:RandellKenney) endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for [pediascape.science](https://pediascape.science/wiki/User:KristanLightfoot) a limit increase, produce a limitation boost demand and reach out to your account group.<br>
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<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Set up permissions 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 permits you to introduce safeguards, avoid harmful material, and assess designs against essential safety requirements. You can implement security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model reactions deployed on [Amazon Bedrock](https://www.wikiwrimo.org) Marketplace and SageMaker JumpStart. You can [produce](https://xtragist.com) a [guardrail utilizing](https://yeetube.com) the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.<br>
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<br>The basic flow involves 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](https://newyorkcityfcfansclub.com) the guardrail check, it's sent to the design for [reasoning](https://followgrown.com). After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the final result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas demonstrate reasoning using this API.<br>
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
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<br>Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (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, pick Model brochure under Foundation models in the navigation pane.
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At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.<br>
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<br>The model detail page offers important details about the model's capabilities, prices structure, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:EdithJoseph92) and application guidelines. You can find detailed usage instructions, consisting of sample API calls and code bits for combination. The design supports different text generation jobs, including content production, code generation, and question answering, using its reinforcement discovering optimization and CoT reasoning capabilities.
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The page also consists of release alternatives and licensing details to help you start with DeepSeek-R1 in your applications.
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3. To begin utilizing DeepSeek-R1, choose Deploy.<br>
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<br>You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
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5. For [yewiki.org](https://www.yewiki.org/User:MayaGinn22) Variety of instances, enter a variety of instances (between 1-100).
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6. For example type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
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Optionally, you can set up innovative security and facilities settings, including virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you might want to examine these settings to line up with your organization's security and compliance requirements.
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7. Choose Deploy to begin [utilizing](http://git.liuhung.com) the design.<br>
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<br>When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
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8. Choose Open in playground to access an interactive user interface where you can try out different triggers and change model criteria like temperature level and maximum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For example, material for reasoning.<br>
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<br>This is an outstanding method to explore the design's reasoning and text generation abilities before integrating it into your applications. The play area offers instant feedback, helping you understand how the model reacts to numerous inputs and [letting](https://dubaijobzone.com) you tweak your triggers for optimal outcomes.<br>
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<br>You can quickly test the model in the play ground through the UI. However, to conjure up the released model 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 deployed DeepSeek-R1 endpoint<br>
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<br>The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually [developed](https://coolroomchannel.com) the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends a demand to produce text based on a user prompt.<br>
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<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
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<br>SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just 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 utilizing](http://leovip125.ddns.net8418) either the UI or SDK.<br>
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<br>[Deploying](https://asesordocente.com) DeepSeek-R1 design through SageMaker JumpStart provides 2 practical methods: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the technique 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 [actions](https://git.lewd.wtf) to deploy 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 users will be prompted to create a domain.
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3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
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<br>The design web browser shows available designs, with details like the company name and design abilities.<br>
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
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Each design card reveals essential 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 applicable), showing that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model<br>
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<br>5. Choose the design card to see 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 release the model.
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About and Notebooks tabs with detailed 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](https://evertonfcfansclub.com) requirements.
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- Usage standards<br>
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<br>Before you deploy the model, it's recommended to examine the design details and license terms to verify compatibility with your usage case.<br>
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<br>6. Choose Deploy to proceed 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 Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial circumstances count, get in the [variety](https://www.nikecircle.com) of instances (default: 1).
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Selecting suitable [circumstances](https://git.nosharpdistinction.com) types and counts is vital for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is picked 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 highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
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11. [Choose Deploy](https://www.jpaik.com) to deploy the model.<br>
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<br>The deployment process can take a number of minutes to finish.<br>
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<br>When deployment is total, your endpoint status will change to [InService](https://hugoooo.com). At this point, the model is ready to accept reasoning demands through the endpoint. You can keep track of the implementation development on the SageMaker [console Endpoints](http://8.211.134.2499000) page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your [applications](https://career.webhelp.pk).<br>
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<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
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<br>To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to deploy 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 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 and run inference with your SageMaker JumpStart predictor<br>
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<br>Similar to Amazon Bedrock, [wavedream.wiki](https://wavedream.wiki/index.php/User:AdriannaBranch) you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:<br>
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<br>Tidy up<br>
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<br>To [prevent unwanted](https://antoinegriezmannclub.com) charges, finish the steps in this area to tidy up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace implementation<br>
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<br>If you released the design utilizing Amazon Bedrock Marketplace, complete the following actions:<br>
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases.
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2. In the Managed releases area, locate the you desire 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 deleting the correct implementation: 1. [Endpoint](https://canadasimple.com) name.
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2. Model name.
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3. [Endpoint](https://repo.amhost.net) 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](http://119.3.70.2075690) if you leave it [running](https://oyotunji.site). 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](https://www.personal-social.com) JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.<br>
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<br>About the Authors<br>
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<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://git.appedu.com.tw:3080) companies develop innovative solutions using [AWS services](https://code.cypod.me) and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the inference efficiency of large [language models](https://www.meetyobi.com). In his spare time, Vivek delights in hiking, watching motion pictures, and attempting various foods.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.bluestoneapps.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://gitlab.dstsoft.net) 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](https://phoebe.roshka.com) working on generative [AI](https://gitea.marvinronk.com) with the Third-Party Model Science group at AWS.<br>
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<br>[Banu Nagasundaram](https://linuxreviews.org) leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://121.43.99.128:3000) center. She is passionate about constructing solutions that help clients accelerate their [AI](http://grainfather.co.uk) journey and unlock company value.<br>
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