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This is a joint blog with AWS and Philips.
Philips is a health technology company focused on improving people’s lives through meaningful innovation. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. It partners with healthcare providers, startups, universities, and other companies to develop technology that helps doctors make more precise diagnoses and deliver more personalized treatment for millions of people worldwide.
One of the key drivers of Philips’ innovation strategy is artificial intelligence (AI), which enables the creation of smart and personalized products and services that can improve health outcomes, enhance customer experience, and optimize operational efficiency.
Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production.
In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker. This platform provides capabilities ranging from experimentation, data annotation, training, model deployments, and reusable templates. All these capabilities are built to help multiple lines of business innovate with speed and agility while governing at scale with central controls. We outline the key use cases that provided requirements for the first iteration of the platform, the core components, and the outcomes achieved. We conclude by identifying the ongoing efforts to enable the platform with generative AI workloads and rapidly onboard new users and teams to adopt the platform.
Philips uses AI in various domains, such as imaging, diagnostics, therapy, personal health, and connected care. Some examples of AI-enabled solutions that Philips has developed over the past years are:
For many years, Philips has been pioneering the development of data-driven algorithms to fuel its innovative solutions across the healthcare continuum. In the diagnostic imaging domain, Philips developed a multitude of ML applications for medical image reconstruction and interpretation, workflow management, and treatment optimization. Also in patient monitoring, image guided therapy, ultrasound and personal health teams have been creating ML algorithms and applications. However, innovation was hampered due to using fragmented AI development environments across teams. These environments ranged from individual laptops and desktops to diverse on-premises computational clusters and cloud-based infrastructure. This heterogeneity initially enabled different teams to move fast in their early AI development efforts, but is now holding back opportunities to scale and improve efficiency of our AI development processes.
It was evident that a fundamental shift towards a unified and standardized environment was imperative to truly unleash the potential of data-driven endeavors at Philips.
AI/ML-enabled propositions can transform healthcare by automating administrative tasks done by clinicians. For example:
Overall, AI/ML promises reduced human error, time and cost savings, optimized patient experiences, and timely, personalized interventions.
One of the key requirements for the ML development and deployment platform was the ability of the platform to support the continuous iterative development and deployment process, as shown in the following figure.
The AI asset development starts in a lab environment, where the data is collected and curated, and then the models are trained and validated. When the model is ready and approved for use, it’s deployed into the real-world production systems. Once deployed, model performance is continuously monitored. The real-world performance and feedback are eventually used for further model improvements with full automation of the model training and deployment.
The more detailed AI ToolSuite requirements were driven by three example use cases:
Building a scalable and robust AI/ML platform requires careful consideration of non-functional requirements. These requirements go beyond the specific functionalities of the platform and focus on ensuring the following:
AI ToolSuite is an end-to-end, scalable, quick start AI development environment offering native SageMaker and associated AI/ML services with Philips HealthSuite security and privacy guardrails and Philips ecosystem integrations. There are three personas with dedicated sets of access permissions:
The platform development spanned multiple release cycles in an iterative cycle of discover, design, build, test, and deploy. Due to the uniqueness of some applications, the extension of the platform required embedding existing custom components like data stores or proprietary tools for annotation.
The following figure illustrates the three-layer architecture of AI ToolSuite, including the base infrastructure as the first layer, common ML components as the second layer, and project-specific templates as the third layer.
Layer 1 contains the base infrastructure:
Layer 2 contains common ML components:
Layer 3 contains project-specific templates that can be created with custom components as required by new projects. For example:
The following diagram highlights the key AWS services spanning multiple AWS accounts for development, staging, and production.
In the following sections, we discuss the key capabilities of the platform enabled by AWS services, including SageMaker, AWS Service Catalog, CloudWatch, AWS Lambda, Amazon Elastic Container Registry (Amazon ECR), Amazon S3, AWS Identity and Access Management (IAM), and others.
The platform uses IaC, which allows Philips to automate the provisioning and management of infrastructure resources. This approach will also help reproducibility, scalability, version control, consistency, security, and portability for development, testing, or production.
SageMaker and associated AI/ML services are accessed with security guardrails for data preparation, model development, training, annotation, and deployment.
The platform ensures data isolation by storing and processing separately, reducing the risk of unauthorized access or data breaches.
The platform facilitates team collaboration, which is essential in AI projects that typically involve cross-functional teams, including data scientists, data science admins, and MLOps engineers.
Role-based access control (RBAC) is essential in managing permissions and simplifying access management by defining roles and permissions in a structured manner. It makes it straightforward to manage permissions as teams and projects grow and access control for different personas involved in AWS AI/ML projects, such as the data science admin, data scientist, annotation admin, annotator, and MLOps engineer.
The platform allows SageMaker access to data stores, which ensures that data can be efficiently utilized for model training and inference without the need to duplicate or move data across different storage locations, thereby optimizing resource utilization and reducing costs.
AWS offers a suite of AI and ML services, such as SageMaker, Amazon SageMaker Ground Truth, and Amazon Cognito, which are fully integrated with Philips-specific in-house annotation tools. This integration enables developers to train and deploy ML models using the annotated data within the AWS environment.
The AI ToolSuite platform offers templates in AWS for various ML workflows. These templates are preconfigured infrastructure setups tailored to specific ML use cases and are accessible through services like SageMaker project templates, AWS CloudFormation, and Service Catalog.
Integration with GitHub enhances efficiency by providing a centralized platform for version control, code reviews, and automated CI/CD (continuous integration and continuous deployment) pipelines, reducing manual tasks and boosting productivity.
Integration with Visual Studio Code provides a unified environment for coding, debugging, and managing ML projects. This streamlines the entire ML workflow, reducing context switching and saving time. The integration also enhances collaboration among team members by enabling them to work on SageMaker projects together within a familiar development environment, utilizing version control systems, and sharing code and notebooks seamlessly.
The platform provides versioning, which helps keep track of changes to the data scientist’s training and inference data over time, making it easier to reproduce results and understand the evolution of the datasets.
The platform also enables SageMaker experiment tracking, which allows end-users to log and track all the metadata associated with their ML experiments, including hyperparameters, input data, code, and model artifacts. These capabilities are essential for demonstrating compliance with regulatory standards and ensuring transparency and accountability in AI/ML workflows.
AWS maintains compliance certifications for various industry standards and regulations. AI/ML specification reports serve as essential compliance documentation, showcasing adherence to regulatory requirements. These reports document the versioning of datasets, models, and code. Version control is essential for maintaining data lineage, traceability, and reproducibility, all of which are critical for regulatory compliance and auditing.
Project-level budget management allows the organization to set limits on spending, helping to avoid unexpected costs and ensuring that the ML projects stay within budget. With budget management, the organization can allocate specific budgets to individual projects or teams, which helps teams identify resource inefficiencies or unexpected cost spikes early on. In addition to budget management, with the feature to automatically shut down idle notebooks, team members avoid paying for unused resources, also releasing valuable resources when they are not actively in use, making them available for other tasks or users.
AI ToolSuite was designed and implemented as an enterprise-wide platform for ML development and deployment for data scientists across Philips. Diverse requirements from all business units were collected and considered during the design and development. Early in the project, Philips identified champions from the business teams who provided feedback and helped evaluate the value of the platform.
The following outcomes were achieved:
As organizations race to adopt the next state-of-the-art in AI, it’s imperative to adopt new technology in the context of the organization’s security and governance policy. The architecture of AI ToolSuite provides an excellent blueprint for enabling access to generative AI capabilities in AWS for different teams at Philips. Teams can use foundation models made available with Amazon SageMaker JumpStart, which provides a vast number of open source models from Hugging Face and other providers. With the necessary guardrails already in place in terms of access control, project provisioning, and cost controls, it will be seamless for teams to start using the generative AI capabilities within SageMaker.
Additionally, access to Amazon Bedrock, a fully managed API-driven service for generative AI, can be provisioned for individual accounts based on project requirements, and the users can access Amazon Bedrock APIs either via the SageMaker notebook interface or through their preferred IDE.
There are additional considerations concerning the adoption of generative AI in a regulated setting, such as healthcare. Careful consideration needs to be given to the value created by generative AI applications against the associated risks and costs. There is also a need to create a risk and legal framework that governs the organization’s use of generative AI technologies. Elements such as data security, bias and fairness, and regulatory compliance need to be considered as part of such mechanisms.
Philips embarked on a journey of harnessing the power of data-driven algorithms to revolutionize healthcare solutions. Over the years, innovation in diagnostic imaging has yielded several ML applications, from image reconstruction to workflow management and treatment optimization. However, the diverse range of setups, from individual laptops to on-premises clusters and cloud infrastructure, posed formidable challenges. Separate system administration, security measures, support mechanisms, and data protocol inhibited a comprehensive view of TCO and complicated transitions between teams. The transition from research and development to production was burdened by the lack of lineage and reproducibility, making continuous model retraining difficult.
As part of the strategic collaboration between Philips and AWS, the AI ToolSuite platform was created to develop a scalable, secure, and compliant ML platform with SageMaker. This platform provides capabilities ranging from experimentation, data annotation, training, model deployments, and reusable templates. All these capabilities were built iteratively over several cycles of discover, design, build, test, and deploy. This helped multiple business units innovate with speed and agility while governing at scale with central controls.
This journey serves as an inspiration for organizations looking to harness the power of AI and ML to drive innovation and efficiency in healthcare, ultimately benefiting patients and care providers worldwide. As they continue to build upon this success, Philips stands poised to make even greater strides in improving health outcomes through innovative AI-enabled solutions.
To learn more about Philips innovation on AWS, visit Philips on AWS.
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