Primary technology recommendations for building a customer-facing machine learning product include React and React Native for the front end, serverless platforms like AWS Amplify or GCP Firebase for authentication and basic server/database needs, and Postgres as the relational database of choice. Serverless approaches are encouraged for scalability and security, with traditional server frameworks and containerization recommended only for advanced custom backend requirements. When serverless options are inadequate, use Node.js with Express or FastAPI in Docker containers, and consider adding Redis for in-memory sessions and RabbitMQ or SQS for job queues, though many of these functions can be handled by Postgres. The machine learning server itself, including deployment strategies, will be discussed separately.

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