- Serving ML Models in Production: Common Patterns - Oct 18, 2021.
Over the past couple years, we've seen 4 common patterns of machine learning in production: pipeline, ensemble, business logic, and online learning. In the ML serving space, implementing these patterns typically involves a tradeoff between ease of development and production readiness. Ray Serve was built to support these patterns by being both easy to develop and production ready.
- Deploying Your First Machine Learning API - Oct 14, 2021.
Effortless way to develop and deploy your machine learning API using FastAPI and Deta.
- ColabCode: Deploying Machine Learning Models From Google Colab - Jul 22, 2021.
New to ColabCode? Learn how to use it to start a VS Code Server, Jupyter Lab, or FastAPI.
- How to Create and Deploy a Simple Sentiment Analysis App via API - Jun 1, 2021.
In this article we will create a simple sentiment analysis app using the HuggingFace Transformers library, and deploy it using FastAPI.
- Deploy a Dockerized FastAPI App to Google Cloud Platform - May 4, 2021.
A short guide to deploying a Dockerized Python app to Google Cloud Platform using Cloud Run and a SQL instance.
- Production-Ready Machine Learning NLP API with FastAPI and spaCy - Apr 21, 2021.
Learn how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER), and see why the author used FastAPI to quickly build a fast and robust machine learning API.