Scaling, Refactoring and fixing a Django MVP for Production
So you've built an AI startup using Async DJango - the MVP looks great and your hand full of users love it. Now you need to clean up the MVP, so you can scale.
This is the Part Two, to building an AI startup with Async DJango - we talk about moving from ChromaDb to a OpenSearch/ElasticSearch, document processing steps to Celery/RabbitMQ, selfhosting via vLLM, migrating from Django templates to a ReactJs APP, better monitoring and logging
Dr Piotr Gryko, studied experimental physics at University College London. His PhD at Imperial College London focused on using biomaterials to self assemble inorganic materials, merging the boundaries of biological systems and machines.
He now specializes in writing software, having worked with image processing, e-commerce, logistics, embedded systems and web development. Always keen to keep learning, he writes open-source code to further develop his understanding of new technologies.
Core technologies:
- Web development (Django)
- Front end web development (reactjs, redux + material ui)
- Data analysis (python, numpy, pandas, jupyter notbooks)
- Devops & sysadmin work (docker, jenkins, digital ocean, postgres replication, ansible, elastic stack)
- Embedded development (C++, arduino, ESP8266, ESP32)