AWS for Industries

Life Sciences and Healthcare Predictive Analytics with Amazon SageMaker Canvas

Artificial intelligence and machine learning (AI/ML) are revolutionizing the Life Sciences and Healthcare industries, from drug discovery and development to patient care. These technologies help to understand disease biology, to accelerate therapeutic discoveries, to optimize resource allocation, and to reduce quality issues, potentially saving lives and reducing healthcare costs. However, building production-grade ML pipelines from typical life sciences and healthcare datasets can present significant challenges.

One challenge often stems from a separation of expertise. Life sciences and healthcare teams possess the domain knowledge needed to understand complex data, but often lack the specialized skills in software or machine learning required to build scalable ML and generative AI pipelines. According to a Gartner survey, IT executives identify talent shortages as the most significant barrier to adopting 64 percent of emerging technologies like AI/ML.

Democratize Machine Learning with Amazon SageMaker Canvas

Amazon SageMaker Canvas is designed to empower non-data scientists to build and deploy ML models at scale. It provides a low-code, visual interface that enables users to create, train, and deploy ML models, including pre-trained foundation models (FMs), without extensive programming or data science expertise. SageMaker Canvas reduces the need for dedicated data science teams for every project, optimizing resource allocation.

Amazon Q Developer, a generative AI assistant, is now available in SageMaker Canvas, enabling helpful guidance throughout your ML journey (from data preparation to model deployment) using conversational chat.

SageMaker Canvas allows organizations to leverage existing domain expertise, potentially lowering overall costs associated with ML implementation. It fosters a collaborative environment that combines industry knowledge with technical expertise.

SageMaker Canvas streamlines AI/ML through four key steps:

  1. Data Import: Access and import data from over 50 sources, including Amazon Simple Storage Service (Amazon S3), Amazon Athena, Amazon Redshift, Snowflake, and Databricks. SageMaker Canvas handles common formats such as tabular, image, time series, and documents.
  2. Chat for Data Prep allows users to create data transformations using natural language without writing code. Users provide a prompt, SageMaker Canvas generates code, then users review the transformation to make any changes and accept or reject the transformation.
  3. One-Click Training: Select your target column, and SageMaker Canvas prepares your data and trains your model automatically—no coding needed. This includes fine-tuning of pre-trained Foundation Models, to build generative AI experiences that take into account your proprietary data.
  4. Quick Predictions: Generate predictions directly in SageMaker Canvas. For further refinement and collaboration, export notebooks and define custom locations for artifacts in Amazon S3.

Bridging the Gap in Life Sciences and Healthcare

SageMaker Canvas bridges the gap between practitioners and data scientists, empowering researchers and specialists to harness ML for their unique challenges. No extensive data science background is required. This capability opens a world of possibilities across various domains in life sciences and healthcare.

Some life sciences and healthcare examples, to be detailed in upcoming installments of this blog include:

  • Disease research – ML can be applied to understand disease progression, to improve early detection and identify new interventions.
  • Lab operations – predictive forecasting models can be used to improve inventory management, to provide greater lab uptime and cost reduction.
  • Drug manufacturing – quality control models can identify anomalies in product testing by predicting and preventing failures.
  • Imaging laboratories – computer vision models can be used to classify cell types including resolving normal compared to disease cells.

These applications demonstrate the versatility of ML in addressing diverse challenges across the industries. By providing accessible ML tools, SageMaker Canvas enables domain experts to directly apply their knowledge to data-driven decision-making, fostering innovation and efficiency in life sciences and healthcare workflows.

Ensuring Security and Compliance

Amazon SageMaker Canvas is designed with security and compliance in mind, which is crucial for Life Sciences and Healthcare organizations dealing with sensitive data. The application runs in a container within an Amazon Virtual Private Cloud (Amazon VPC), providing a secure environment for your ML workflows.

For organizations with stringent security requirements, SageMaker Canvas can be configured to run without public internet access, using Amazon VPC endpoints to securely access AWS resources. This setup allows for tighter control over data access and job containers, verifying that sensitive information remains protected throughout the ML process.

Furthermore, SageMaker Canvas integrates with AWS Identity and Access Management (IAM), allowing you to fine-tune permissions and access controls to meet your organization’s specific security policies.

For a detailed overview of security features and configuration options, please refer to the Configure Amazon SageMaker Canvas in a VPC without internet access documentation.

Conclusion

Using SageMaker Canvas can help Life Sciences and Healthcare quickly identify potential disease risks and analyze complex imaging datasets, accelerating disease research. It can also help manage and predict lab operations and limit deviations in manufacturing. Through the use of natural language chat and with no coding expertise needed, it helps save on costs and operational time—improving team efficiencies.

As the Life Sciences and Healthcare industries continue to evolve, tools like Amazon SageMaker Canvas will play a crucial role in democratizing ML and generative AI capabilities, enabling organizations to leverage the power of predictive analytics across a wide range of applications and use cases.

Contact an AWS Healthcare Representative or Life Sciences Representative to know how we can help accelerate your business.

Get Started with Amazon SageMaker Canvas

Ready to explore how Amazon SageMaker Canvas can benefit your Life Sciences or Healthcare organization? Here are some next steps:

  • Complete the Getting Started with Amazon SageMaker Canvas tutorial
  • Generate Machine Learning Predictions Without Writing Code using our step-by-step guide
  • Explore SageMaker Canvas sample datasets to practice with pre-prepared data

Coming soon – No-Code ML Approach to Predict Heart Disease with Amazon SageMaker Canvas

James Gaines

James Gaines

James Gaines is a Senior Solutions Architect for Healthcare and Life Sciences at AWS. He has a background in highly regulated environments, including the Department of Defense and pharmaceutical industry. James holds all active AWS Certifications and specializes in cloud migrations, application modernization, and advanced analytics to drive innovation in Healthcare and Life Sciences.

Lee Tessler

Lee Tessler

Lee Tessler is a Principal Technology Strategist for the Healthcare & Life Sciences industry at AWS. Lee has a background in math and biology and a PhD in Computational Biology, with over 15 years of experience in the biotechnology industry. He is focused on developing new approaches to distributed computing in Healthcare and Life Sciences to make the world healthier, cleaner, and safer.