Blog - Yseop 2024年12月05日
How Yseop Leveraged AWS to Develop a Unique Generative AI Application for Document Generation
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Yseop与全球领先的制药公司合作,利用其企业平台Yseop Copilot自动化药物研发过程中的关键监管文档创建,加速药物上市速度。Yseop利用AWS的强大安全性和可扩展性,结合大型语言模型(LLM)技术,提升了内容生成能力,满足了制药行业的严格安全标准。通过与AWS的合作,Yseop显著提高了生产力,加快了药物交付速度,并利用Amazon Bedrock等服务实现了快速部署和模型管理,有效降低了成本,提升了效率。

🚀 **Yseop与全球领先药企合作,利用Yseop Copilot自动化药物研发文档创建:**Yseop Copilot作为数字助手,自动化生成关键的监管文档,帮助药企节省大量时间和精力,加速药物上市进程,并已在多个全球前20大制药公司中使用。

🤝 **Yseop与AWS深度合作,利用AWS的强大功能和安全优势:**Yseop是AWS合作伙伴网络(APN)成员,充分利用了AWS的安全性和可扩展性,并扩展使用AWS来增强其大型语言模型(LLM)生态系统、基础设施和技术专业知识,以满足制药行业严格的安全标准。

💡 **利用Amazon Bedrock加速模型部署和管理:**Yseop利用Amazon Bedrock快速部署预训练模型,并将其无缝集成到现有基础设施中,从而加速创新步伐。Bedrock提供了一系列功能,包括检索增强生成(RAG)、代理和微调功能,有效降低了成本,提高了效率。

☁️ **Bedrock与SageMaker优势互补,构建强大的AI生态系统:**Bedrock提供了对各种预训练模型的便捷访问,加速了开发过程,而SageMaker则提供了对自定义模型的完全控制。Yseop根据不同需求选择合适的工具,例如,当需要保证响应格式的一致性和高质量时,仍然会使用SageMaker。

🌟 **Yseop持续创新,利用AI技术提升效率和生产力:**通过采用Bedrock,Yseop显著提高了效率和生产力,使LLM团队能够专注于推动业务增长的重要任务。Bedrock和SageMaker的组合使用创建了一个功能强大且灵活的AI生态系统,为客户提供了卓越的价值。

Yseop has partnered with the world’s leading pharmaceutical companies to expedite the delivery of life-saving therapies and save thousands of hours in medical writing and review. Yseop Copilot, its enterprise platform, serves as a digital colleague, automating content creation for crucial regulatory documentation throughout drug development. The platform integrates seamlessly into medical writing tools and workflows and is already in use by several top 20 pharmaceutical companies.

As pioneers in Natural Language Processing (NLP) technology, Yseop leverages pre-trained LLM models specifically designed for the BioPharma industry. This multimodal approach marks a significant advancement for life sciences firms, enabling medical writers to boost productivity and focus on strategic tasks in a secure environment. Yseop Copilot utilizes proprietary prompts and validation methods to guarantee writing accuracy and traceability. Offering each customer a fully secure, private hosting environment.

Yseop and AWS: Empowering Innovation in Large Language Models 

As a member of the AWS Partner Network (APN), Yseop has taken advantage of AWS’s robust security and scalability from the start. Now, Yseop is expanding its AWS usage to enhance its Large Language Model (LLM) ecosystem, infrastructure, and technical expertise.

By leveraging AWS, Yseop integrates LLM models to create a broader range of scientific content, all while meeting the strict security standards of the pharmaceutical industry. This collaboration empowers regulatory and medical writers at the world’s top pharmaceutical companies to significantly increase their productivity and accelerate the delivery of therapeutics to patients in need.

Key benefits of the Yseop and AWS partnership include:

    Accelerating time-to-value with a ready-to-use SaaS enterprise platform powered by AWS.Safely scaling content generation across organizations using AWS managed services and tools such as Amazon EKS, Amazon RDS, and Infrastructure as Code CDK.Managing comprehensive data and document flows with enhanced security features.Accessing cutting-edge AI capabilities with a model-agnostic SaaS supported by AWS SageMaker, AWS Inferentia, and AWS EC2.

AWS provides Yseop and its clients with a fully managed, private cloud environment, essential for the highly regulated life sciences industry. 

Yseop’s Journey with AWS SageMaker

As the security and infrastructure needs of the life sciences sector evolved over the last five years, Yseop reached an inflection point regarding our hosting and infrastructure requirements. On the verge of a breakthrough in Generative AI for pharma, our team grappled with the limitations of our existing technology stack.

For years, Yseop relied on AWS SageMaker to deploy our custom AI models. This service provided full control of the AI stack, from training to testing and deployment, and supported us through numerous projects and implementations. However, starting last year, increasing project complexity and client demands in GenAI necessitated the use of multiple models for various use cases. As a result, we needed a more scalable, efficient, and cost-effective solution that allows fast evaluations of new frontier models and immediate productization.

When Bedrock reached General Availability (GA) in September 2023, Yseop began considering it a viable alternative. The specific models we needed became available in March 2024. With recommendations and support from our AWS contacts, Bedrock was chosen to address that need, and we transitioned smoothly within a few weeks. 

In October, Dominique Mariko, VP of Data Science & AI, showcased Yseop's transition to Amazon Bedrock at AWS Executive Forum in Paris, highlighting the innovative Yseop Copilot SaaS solution.

Benefits of Bedrock for Yseop  

Bedrock handles rapid deployments of pre-trained models, seamlessly integrating within our existing infrastructure. Most importantly, the application adapts to future needs, accelerating our pace of innovation. The move to Bedrock reflected Yseop’s strategic foresight and commitment to excellence, allowing the team to overcome previous scalability constraints and enhance technical ingenuity. 

Bedrock is tailored to our unique life sciences requirements, launching models highly relevant to our industry. Clause 3.5, a competitor to GPT-4, stands out for its quality and sophistication. These pre-trained models are designed to scale, ensuring we meet customers’ demands without interruption.

While SageMaker remains part of Yseop’s toolkit for custom models, Bedrock’s rapid deployment capabilities better align with Yseop’s operational needs for developing applications securely. We can easily iterate on new models versions like Claude 3.5 and Llama 3.1 without needing extensive internal resources for updates and maintenance. These models are readily accessible and regularly upgraded, allowing our teams to focus on developing applications and leveraging the latest AI capabilities to remain competitive. Additionally, the Bedrock Custom Model Import feature facilitates the effortless integration of models built on Sagemaker.

Enhanced Capabilities with Bedrock

Bedrock brings a range of features that significantly enhance our operations, including comprehensive monitoring capabilities. Bedrock’s flexible pricing model, based on token consumption, ensures we only pay for what we use. This approach is especially beneficial for our inference tasks, where model utilization can vary significantly. The platform automatically adjusts resources based on the number of requests, ensuring optimal performance and efficient load management. This flexibility allows us to manage our budget more effectively while maintaining high performance. 

Key features of Bedrock, such as Retrieval-Augmented Generation (RAG), agents, and fine-tuning capabilities, complement SageMaker’s strengths. RAG allows for seamless content retrieval through advanced integration with NoSQL databases, vector databases and content storage systems (Knowledge Bases), providing more accurate and relevant responses, enhancing the overall user experience.

Bedrock integrates smoothly with other AWS data services, aligning perfectly with our existing AWS-based architecture and simplifying our daily management. The consistency of having a single point of entry, regardless of the model, streamlines our operations, allowing our team to adapt quickly and efficiently to different models. 

In November, Pierre-Louis Durel, VP of Corporate Development, showcased Yseop's use of AWS tools at AWS GenAI Day, highlighting Generative AI's transformative impact on SaaS.

Complementary Solutions: Bedrock and SageMaker 

We see Bedrock and SageMaker as complementary solutions. Bedrock offers easy access to a variety of pre-trained models, saving time and accelerating development, while SageMaker provides full control over our custom models. The transition to Bedrock was about using the right tool for the right task. For example, while we anticipate the need for future grammar constraints to ensure consistent and high-quality responses, this feature is not yet available on Bedrock. Therefore, we continue to utilize SageMaker for specific applications where consistent response formatting is crucial.

By adopting Bedrock, Yseop has significantly improved efficiency and productivity, allowing our LLM team to concentrate on high-impact tasks that drive business growth. The ability to quickly deploy and manage pre-trained models without extensive internal resources has been transformative, enabling us to remain competitive in a rapidly evolving industry. The combined use of Bedrock and SageMaker creates a versatile and robust AI ecosystem. This strategic approach ensures that we meet a wide range of AI requirements, delivering superior value to our clients. 

Conclusion 

Yseop’s transition to Bedrock, AWS’s latest AI and machine learning platform, has been transformative for our team. This strategic move, following intense discussions and rigorous evaluations, demonstrates our foresight and unwavering commitment to excellence. Yseop and AWS align in their vision for advancing Generative AI in the life sciences sector. The Bedrock platform allows us to overcome scalability limitations and advance our technical capabilities.

Our existing partnership with AWS played a significant role in this transition. With AWS’s extensive support, integrating Bedrock into our operations was effortless. This integration streamlines our model deployment and management processes, enabling us to focus more on our core strength—innovation. 

The post How Yseop Leveraged AWS to Develop a Unique Generative AI Application for Document Generation appeared first on Yseop.

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Yseop AWS 大型语言模型 药物研发 生成式AI
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