Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are thrilled 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 deploy DeepSeek [AI](http://120.36.2.217:9095)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion [specifications](https://gitea.lolumi.com) to develop, experiment, and properly scale your generative [AI](https://gitlab.chabokan.net) 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 [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:ElviraLamarr892) SageMaker JumpStart. You can follow similar steps 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) developed by DeepSeek [AI](https://euvisajobs.com) that utilizes support discovering to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key identifying feature is its support learning (RL) step, which was utilized to refine the design's actions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, [suggesting](https://rocksoff.org) it's equipped to break down intricate inquiries and reason through them in a detailed way. This guided thinking process permits the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to create structured actions while focusing on interpretability and user interaction. With its [wide-ranging abilities](http://moyora.today) DeepSeek-R1 has captured the market's attention as a versatile text-generation model that can be incorporated into different workflows such as representatives, rational thinking and information analysis 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 permits activation of 37 billion criteria, allowing effective reasoning by routing queries to the most appropriate specialist "clusters." This approach enables the design to focus on different problem domains while maintaining general efficiency. 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 deploy the model. ml.p5e.48 xlarge comes with 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 model 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 designs to imitate the [behavior](https://dolphinplacements.com) and thinking patterns of the larger DeepSeek-R1 model, using it as a [teacher design](https://fishtanklive.wiki).<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 use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and evaluate designs against essential safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://moojijobs.com) applications.<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 circumstances. To examine if you have quotas for [links.gtanet.com.br](https://links.gtanet.com.br/terilenz4996) P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for [endpoint usage](https://demo.playtubescript.com). Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limit increase, create a limit boost demand and connect to your account team.<br> |
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Set up authorizations to use 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 harmful content, and evaluate designs against essential security requirements. You can carry out safety measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design actions 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 produce the guardrail, see the GitHub repo.<br> |
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<br>The general 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 the [guardrail](https://ansambemploi.re) check, it's sent out to the design for reasoning. After receiving the [design's](http://carpediem.so30000) output, another [guardrail check](http://gitlab.mints-id.com) is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the [intervention](http://globalnursingcareers.com) and whether it occurred at the input or output stage. The examples showcased in the following sections show 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 provides 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 steps:<br> |
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<br>1. On the Amazon Bedrock console, catalog under Foundation designs 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 choose the DeepSeek-R1 design.<br> |
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<br>The model detail page supplies important details about the model's capabilities, rates structure, and execution standards. You can find detailed use guidelines, consisting of sample API calls and code bits for combination. The model supports various text [generation](http://110.90.118.1293000) jobs, consisting of content production, code generation, and concern answering, utilizing its support learning optimization and CoT reasoning abilities. |
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The page likewise includes release choices and licensing details to help you start with DeepSeek-R1 in your applications. |
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3. To start using DeepSeek-R1, choose Deploy.<br> |
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<br>You will be triggered to configure the implementation 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 Number of instances, enter a number of instances (between 1-100). |
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6. For example type, pick your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. |
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Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, [surgiteams.com](https://surgiteams.com/index.php/User:CathleenMadison) for production deployments, you might desire 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 the model.<br> |
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<br>When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. |
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8. Choose Open in play area to access an interactive interface where you can explore various prompts and adjust model specifications like temperature level and optimum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, content for reasoning.<br> |
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<br>This is an excellent method to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The play area supplies immediate feedback, assisting you understand how the design responds to various inputs and letting you tweak your triggers for [kigalilife.co.rw](https://kigalilife.co.rw/author/erickkidman/) optimal outcomes.<br> |
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<br>You can quickly evaluate the design in the [play ground](https://propveda.com) through the UI. However, to invoke the released design programmatically with any [Amazon Bedrock](https://www.complete-jobs.com) APIs, you need to get the endpoint ARN.<br> |
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<br>Run inference using guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends a request to generate text based on a user timely.<br> |
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<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br> |
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<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](https://gitea.nongnghiepso.com) to your use case, with your information, and release them into production utilizing either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 convenient methods: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you pick the technique that finest 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, 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 model web browser displays available designs, with details like the provider name and design capabilities.<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 reveals key details, consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- Task category (for instance, [ratemywifey.com](https://ratemywifey.com/author/christenaw4/) Text Generation). |
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Bedrock Ready badge (if applicable), suggesting that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model<br> |
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<br>5. Choose the [design card](https://dakresources.com) to view the design 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 provider 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 consists of important 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 release the model, it's suggested to evaluate the model details and license terms to verify compatibility with your use case.<br> |
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<br>6. Choose Deploy to proceed with implementation.<br> |
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<br>7. For Endpoint name, utilize the automatically produced name or develop 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, enter the variety of instances (default: 1). |
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Selecting suitable circumstances types and counts is vital for cost and performance optimization. Monitor your release to change 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 seclusion remains in place. |
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11. Choose Deploy to release the model.<br> |
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<br>The release procedure can take a number of minutes to finish.<br> |
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<br>When release is total, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can monitor the deployment development 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 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](http://45.45.238.983000) SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a [detailed](http://103.197.204.1633025) code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and run 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, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can [develop](http://git.spaceio.xyz) a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br> |
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<br>Clean up<br> |
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<br>To avoid [undesirable](https://vibestream.tv) charges, finish the actions in this section to tidy up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace deployment<br> |
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<br>If you [deployed](https://git.sortug.com) 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, select Marketplace releases. |
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2. In the Managed deployments section, find the [endpoint](https://washcareer.com) 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 right implementation: 1. [Endpoint](https://git.ashcloudsolution.com) name. |
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2. Model name. |
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3. [Endpoint](https://git.tool.dwoodauto.com) status<br> |
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<br>Delete the SageMaker JumpStart predictor<br> |
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<br>The [SageMaker JumpStart](https://www.happylove.it) design you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br> |
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<br>Conclusion<br> |
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<br>In this post, we explored how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://jobsantigua.com) or Amazon Bedrock Marketplace now to get started. 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 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 assists emerging generative [AI](https://www.oddmate.com) business build ingenious solutions using AWS services and sped up calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning performance of large language designs. In his spare time, Vivek enjoys treking, seeing motion pictures, and attempting various cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://119.45.195.106:15001) Specialist Solutions Architect with the Third-Party Model [Science](http://112.74.93.6622234) group at AWS. His location of focus is AWS [AI](http://106.39.38.242:1300) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [Science](http://123.57.58.241) and Bioinformatics.<br> |
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<br>Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://cloud-repo.sdt.services) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitlab.ucc.asn.au) center. She is passionate about constructing solutions that help customers accelerate their [AI](http://119.29.169.157:8081) journey and unlock organization value.<br> |
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