Learn how Snowflake, a cloud-based data warehousing company, is transforming the life sciences industry with solutions for big data challenges. Dive into the evolution of technology in biotech, the role of AI and cloud computing in drug discovery, and the intersection of technology and life sciences. Discover how Snowflake integrates with AI and machine learning to revolutionize workflows in drug discovery.
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Quick takeaways
Snowflake supports AI partnerships for efficient data management in biotech.
Cortex AI integration enables rapid deployment of PyTorch models for life sciences analysis.
Delegation of AI model hosting to NVIDIA enhances protein folding predictions in drug discovery.
Deep dives
Leveraging Snowflake for Life Sciences Data Challenges
Snowflake, a cloud-based warehousing company, supports storing and analyzing large data sets for biotech through partnerships with AI companies like Anas, offering container services allowing easy management of proprietary models in the cloud. This aids in AI adoption in drug discovery operations.
Managing Unstructured Data in Snowflake with Cortex AI
Snowflake introduces capabilities like Cortex AI for managing new modalities of data like smile strings or images often used in life sciences. This integration allows accessing PyTorch models, enabling rapid prototype deployment for complex life sciences analyses.
Utilizing Container Services for AI Model Deployment
Snowflake's container services enable the delegation of AI model hosting to external entities like NVIDIA, providing access to advanced models like BioNemo for better protein folding predictions. This allows efficient data management for high-throughput drug discovery processes.
Streamlining Clinical Protocols with Snowflake's Vector Data
Snowflake's capability to store embeddings in vector databases allows efficient similarity searches for clinical protocols. By using containers and AI models, life sciences companies can streamline data connectivity and accelerate research tasks.
Forecasting Future Trends: Wearable Probes and AI Monitoring
Emerging trends in life sciences foresee wearable probes that monitor cellular interactions through streaming data back to the cloud for analysis. The integration of image-based monitoring and AI promises advancements in real-time health diagnostics and personalized treatments.
The growing use of large datasets and ML in the life sciences has created new demand for data technologies. Snowflake is a cloud-based data warehousing company that provides a platform for storing and analyzing large volumes of data.
Harini Gopalakrishnan is the Field CTO of Life Sciences at Snowflake. She joins the show to talk about data challenges and solutions in biotech.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .