Routing Traffic Between FastAPI Pods in Kubernetes

This is a quick tutorial to show how to route traffic between Kubernetes Pods. Both Pods are running FastAPI endpoints. I show how to create Deployment and Service elements for Kubernetes Pod, and how to refer to that service from another Pod to execute HTTP call.

 

Human Pose Estimation with TensorFlow.js and React

Want to learn #MachineLearning and #React by doing? My @ManningBooks liveProject 'Human Pose Estimation with TensorFlow.js and React' is published. Free access to Manning books is included. Try it here.

FastAPI Running on Kubernetes Pod

Step-by-step tutorial where I explain and show how to run FastAPI app on Kubernetes Pod. I keep it simple. I explain when it makes sense to use multiple containers in a single Pod and when you should put containers into different Pods.

 

Dockerfile and Docker Compose Tutorial for MLOps

This is the tutorial, where I talk about MLOps, explain the difference between Dockerfile and Docker Compose YML definition file. I briefly explain what you should be aware of if planning to move your solution to Kubernetes in the future. I explain in simple words, what is Dockerfile and when Docker Compose is useful. The sample service is based on TensorFlow functionality, where we call model predict function to process serving request.

 

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.