Organizations of all sizes and in all industries are using generative AI to fundamentally transform their business environments with reimagined customer experiences, increased employee productivity, new levels of creativity, and optimized business processes. I’m considering doing so. A recent study by Telecom Advisory Services, a globally recognized research and consulting firm specializing in economic impact research, shows that cloud-enabled AI could add more than $1 trillion to global GDP from 2024 to 2030. Shown.
Organizations are looking to accelerate the process of building new AI solutions. Build, train, and deploy generative AI models using fully managed services such as Amazon SageMaker AI. They often want to integrate their specialized AI development tools of choice to build models on SageMaker AI.
However, the process of identifying the right application is complex and demanding, and it requires significant effort to ensure that the selected application meets an organization’s specific business needs. Deploying, upgrading, managing, and extending selected applications also takes considerable time and effort. To adhere to strict security and compliance protocols, organizations also need to keep their data within security boundaries without storing it on infrastructure owned by software-as-a-service (SaaS) providers. there is.
This increases the time it takes for customers to gain insights from their data. Our customers want a simple and secure way to find the best applications, integrate selected applications into machine learning (ML) and generative AI development environments, and manage and scale their AI projects.
Introducing Amazon SageMaker Partner AI Apps
Today, we’re excited to announce that AWS partner AI apps are now available in SageMaker. Now you can privately and securely discover, deploy, and use these AI apps without leaving SageMaker AI, helping you develop high-performing AI models faster.
Industry-leading app provider
The first group of partners and applications shown in the following diagram includes Comet and its model experiment tracking application, Deepchecks and its Large Language Model (LLM) quality and evaluation application, Fiddler and its model observability application, and Lakera and its Its AI security application.
controlled and safe
These applications are fully managed by SageMaker AI, so customers don’t have to worry about provisioning, scaling, or maintaining the underlying infrastructure. SageMaker AI ensures that sensitive data remains entirely within each customer’s SageMaker environment and is never shared with third parties.
Available in SageMaker AI and SageMaker Unified Studio (preview)
Data scientists and ML engineers can access these applications through Amazon SageMaker AI (formerly Amazon SageMaker) and SageMaker Unified Studio. This feature gives data scientists and ML engineers seamless access to the tools they need, increasing productivity and accelerating the development and deployment of AI products. It also enables data scientists and ML engineers to get more out of their models by seamlessly collaborating with colleagues in data and analytics teams.
Seamless workflow integration
Direct integration with SageMaker AI provides a frictionless user experience from model building and deployment to continuous operational monitoring, all within the SageMaker development environment. For example, data scientists can run experiments in SageMaker Studio or SageMaker Unified Studio Jupyter notebooks and use Comet ML apps to visualize and compare those experiments.
Streamlined access
AWS Credits accelerate your adoption and expansion of AI observability by allowing you to use partner apps without going through lengthy procurement and approval processes.
Application details
By integrating these AI apps within SageMaker Studio, you can build AI models and solutions without leaving the SageMaker development environment. Take a look at the first group of apps announced at re:Invent 2024.
comet
Comet provides AI developers with an end-to-end model evaluation solution with best-in-class tools for experiment tracking and model production monitoring. Comet has been trusted by corporate customers and academic teams since 2017. Within SageMaker Studio, Notebooks, and Pipelines, data scientists, ML engineers, and AI researchers can use Comet’s robust tracking and monitoring capabilities to monitor the lifecycle of models from training to production, providing transparency and Reproducibility of ML workflows.
You can access Comet UI directly from SageMaker Studio and SageMaker Unified Studio without providing additional credentials. The app’s infrastructure is deployed, managed, and supported by AWS, providing a comprehensive experience and seamless integration. This means that each Comet deployment through SageMaker AI is securely isolated and automatically provisioned. Seamlessly integrate Comet’s advanced tools without changing your existing SageMaker AI workflows. For more information, please visit Comet.
deep check
Deepchecks specializes in LLM assessments. Validation features include automatic scoring, version comparison, and automatically calculated metrics such as relevance, coverage, and context-based properties. These features allow organizations to rigorously test, monitor, and improve their LLM applications while maintaining complete data sovereignty.
Deepchecks’ state-of-the-art automated scoring capabilities for LLM applications, combined with the infrastructure and purpose-built tools provided by SageMaker AI for each step of the ML and FM lifecycle, helps AI teams improve model quality. You can and compliance.
Starting today, organizations using AWS can immediately use Deepchecks’ LLM assessment tools within their environments, minimizing security and privacy concerns because their data is fully contained within the AWS environment. You can suppress it. This integration also eliminates the overhead of third-party vendor onboarding as legal and procurement aspects are streamlined by AWS. For more information, please visit Deepchecks.
violin player AI
Fiddler AI Observability solution enables data science, engineering, and line-of-business teams to validate, monitor, analyze, and improve ML models deployed to SageMaker AI.
Fiddler’s advanced features allow users to track model performance, monitor data drift and integrity, and receive alerts for instant diagnostics and root cause analysis. This proactive approach allows teams to quickly resolve issues and continuously improve model reliability and performance. For more information, see Fiddler.
Rakela
Lakera partners with enterprises and high-growth technology companies to enable generative AI transformation. Lakera’s application Lakera Guard provides real-time visibility, protection, and control for generative AI applications. Lakera Guard protects sensitive data, mitigates immediate attacks, and creates guardrails to ensure generative AI always behaves as expected.
Starting today, you can set up a dedicated instance of Lakera Guard within SageMaker AI to ensure data privacy and low-latency performance with the flexibility to scale with the evolving needs of your generative AI applications. You will be able to do it. For more information, please visit Lakera.
See how customers use partner apps
“Natwest Group’s AI/ML team leverages SageMaker and Comet to rapidly develop customer solutions, from rapid fraud detection to deep analysis of customer interactions. By becoming a partner app, we have streamlined our technology, enhanced developer workflows, and improved experiment tracking and model monitoring, resulting in better results and experiences for our customers.”
– Greig Cowan, Head of AI and Data Science, NatWest Group.“Amazon SageMaker plays a critical role in the development and operation of Ping Identity’s homegrown AI and ML infrastructure. The AI app capabilities of SageMaker partners allow us to privately deliver faster, more effective ML-powered capabilities. Now we can offer it to our customers as a fully managed service, reducing operational overhead while supporting their strict security and privacy requirements.”
– Ran Wasserman, Principal Architect, Ping Identity.
Start building with AI apps from AWS partners
Amazon SageMaker AI provides access to highly curated apps from industry-leading providers that are designed and certified to run natively and privately on SageMaker AI. Data scientists and developers can quickly find, deploy, and use these applications within SageMaker AI and the new integrated studio to accelerate building ML and generative AI models.
All available SageMaker partner AI apps can be accessed directly from SageMaker AI and SageMaker Unified Studio. Click to see specific app features, license terms, and estimated deployment costs. After subscribing, you can configure the infrastructure on which your app runs by choosing a deployment tier and additional configuration parameters. Once the app provisioning process is complete, you can assign access rights to users and they can use the app in SageMaker Studio and SageMaker Unified Studio environments.
About the author
gwen chen I am a Senior Generative AI Product Marketing Manager at AWS. She started working on AI products in 2018. Gwen launched MLOps, an NLP-powered app building product, a generative AI-powered assistant for data integration and model building, and inference capabilities. Gwen is a graduate of Duke University and UNC Kenan-Flagler’s dual master’s programs in science and business. Gwen enjoys listening to podcasts, skiing, and dancing.
naufal meal I am a Senior Generative AI/ML Specialist Solutions Architect at AWS. His focus is on helping customers build, train, deploy, and migrate ML workloads to SageMaker. Previously, he worked at a financial services institution developing and operating large-scale systems. He enjoys ultra-endurance running and cycling.
Kunal Jha I’m a senior product manager at AWS. He is focused on building Amazon SageMaker Studio as the best IDE for all ML development steps. In his free time, Kunal enjoys skiing, scuba diving, and exploring the Pacific Northwest. You can find him on LinkedIn.
Eric Peña is a Senior Technical Product Manager on the AWS Artificial Intelligence Platform team working on Amazon SageMaker Interactive Machine Learning. His current focus is on IDE integration in SageMaker Studio. He holds an MBA degree from MIT Sloan and enjoys playing basketball and football outside of work.
Alkaprava de is a manager who leads the SageMaker Studio Apps team at AWS. He has been with Amazon for over 9 years and is currently working on improving the Amazon SageMaker Studio IDE experience. You can find him on LinkedIn.
Huang Zuo Won I’m a software development manager at AWS. He has been with Amazon for over 5 years, focusing on building SageMaker Studio apps and IDE experiences. You can find him on LinkedIn.
