Hugging Face Course and Pretrained Model Fine-Tuning

Hugging Face team recently released an online course about transformers, pretrained model fine-tuning, and sharing models on the Hugging Face hub. I went through the first part of the course related to model fine-tuning. I explain what changes I did for my previous sample related to Hugging Face model fine-tuning, based on knowledge learned from this course.

 

Serving ML Model with Docker, RabbitMQ, FastAPI and Nginx

In this tutorial I explain how to serve ML model using such tools as Docker, RabbitMQ, FastAPI and Nginx. The solution is based on our open-source product Katana ML Skipper (or just Skipper). It allows running ML workflow using a group of microservices. It is not limited to ML, you can run any workload using Skipper and plugin your own services. You can reach out to me if you got any questions.

 

TensorFlow Decision Forests Example

With TensorFlow Decision Forests we can handle structured data without much preprocessing. There is no need to normalize numeric values, one-hot encode categorical values, or set magic values to replace missing data. I give it a try and run this new feature of TensorFlow in this video. The demo is based on the Titanic dataset taken from Kaggle.