Design and implement a scalable, production-ready AI/ML / Data Science architecture that enables the client to derive insights and predictions from large volumes of diverse data sources, using deep learning models. The solution should handle everything from raw data ingestion to real-time inference.
The client requires a scalable end-to-end AI/ML solution that transforms raw data into actionable insights using deep learning models. The architecture must support:
Data Ingestion: Collect data from business systems, IoT sensors, mobile apps, social media, and partners.
Data Preparation: Pre-process and organize data into training and testing sets using automated pipelines.
Model Training: Use distributed deep learning frameworks (e.g., TensorFlow) with hyperparameter tuning and monitoring for optimal model development.
Model Deployment & Inference: Deploy trained models to production for real-time predictions using new data.
The solution should handle large-scale data, support iterative model improvements, and deliver fast, production-grade inference with minimal latency.