Author: Regis Nde Tene (Chopinregis)
Status: Draft
Duration: 2 Week Immersion
Develop an AI-powered system on Google Cloud Platform that matches cancer patients to relevant clinical trials using RAG and fine-tuned language models. The system incorporates multi-agent collaboration for comprehensive trial analysis and employs MLOps principles for reliable deployment and monitoring, ensuring alignment with AI safety standards.
This stage successfully laid the groundwork for the AI-powered clinical trial matching system on GCP. We set up a new GCP project, configured IAM with a service account, enabled crucial APIs, and established secure data storage using Cloud Storage for raw files and BigQuery for structured data, completing the initial data ingestion.
This stage successfully established the core Retrieval-Augmented Generation (RAG) pipeline on GCP. We learned to ingest and preprocess unstructured clinical trial data, generate vector embeddings using Vertex AI, store and retrieve these embeddings from Vertex AI Vector Search, and integrate the retrieved context with an LLM for factual, grounded responses. This mitigates LLM hallucinations and ensures relevant information is used for clinical trial matching.
This stage successfully designed and implemented a sophisticated multi-agent system, leveraging frameworks like LangGraph, to perform in-depth analysis of clinical trial protocols against patient profiles. By assigning specialized roles and orchestrating their collaborative workflow, we significantly enhanced the accuracy, comprehensiveness, and robustness of the trial matching process, demonstrating advanced skills in multi-agent system design and applied AI.
This stage focused on enhancing the core intelligence of our clinical trial matching system. We successfully fine-tuned a language model on a specialized medical dataset, tailoring its understanding to the nuances of clinical trials and patient information. Through rigorous evaluation, we validated its improved performance, culminating in a robust, domain-aware model ready for integration and deployment.
This stage successfully transitioned the AI-powered clinical trial matching system from development to a production-ready state on GCP. Key MLOps principles were applied, including containerization, automated CI/CD pipelines with Cloud Build, and deployment to Cloud Run. Crucially, robust monitoring and logging were established, complemented by AI-specific performance and data drift detection, ensuring the system's continuous operation, reliability, and accuracy.
This stage focused on transforming our core AI components into production-ready microservices. We designed and implemented RESTful APIs for both the RAG system and the fine-tuned LLM, then containerized these services using Docker. This ensures modularity, simplifies integration, and establishes a robust foundation for scalable deployment and MLOps practices on GCP.