GenAI Presales Solution Proposal : Healthcare Industry
Rakesh Jha - Product Head (Chief Architect)
Product Head | Driving Industry Innovation | Architecting Multi-Geography B2B2C Ecommerce Ecosystems | Expert in Business Strategy, Market Analysis, Technical Leadership and Revenue Generation
Healthcare Assistant is a mobile application that allows users to ask healthcare-related questions and receive AI-generated responses. The goal was to create an AI-driven platform that can provide reliable healthcare advice in real time, recommend treatments, and suggest nearby doctors if needed.
I worked as GenAI Presales Solution Architect professionally working on hashtag#GCP hashtag#GenAI based hashtag#RFP hashtag#proposal as a hashtag#Presales hashtag#Solution hashtag#Architect for Healthcare Domain.
1. Google Cloud (Vertex AI, TPUs, Cloud Storage, Cloud Functions, Dataflow)
2. Google Healthcare API, Maps API, Firebase
3. Hugging Face Transformers, TensorFlow or PyTorch, MLOps
4. React Native, Node.js for mobile app and backend
5. Multilingual Support: Google Cloud Translation API
6. Monitoring Tools : Vertex AI Monitoring, Google Cloud Logging and Monitoring
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Key Takeaways from Implementations Proposal :
1. Google Cloud (Vertex AI): Played a critical role in this case for training, deploying, and serving the GenAI models.
2. Data Preprocessing: Proper preprocessing using Google Dataflow ensured that the datasets were clean and structured for training.
3. Custom Model Fine-Tuning: Implementations required custom fine-tuning of pre-trained models (GPT-3, BERT) to achieve domain-specific performance
4. Frontend and Backend Integration: Seamless integration with mobile and web apps using React and Node.js ensured smooth interaction with end users.
5. hashtag#Timeframe: For Projects MVP/PoC timeline and costing, let me know one to one. It will take around XXX months to implement, depending on the complexity of the AI models and integration requirements.
Any Guideline, suggestion are most welcome.
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