ML in Production - Page 6 of 6 - Best practices for building real world machine learning systems

Storing Metadata from Machine Learning Experiments

Before deploying a machine learning model to production, data scientists spend a large amount of time conducting experiments. These experiments, which include determining which class of models to use and what types of features to include, produce a number of… Read More

Using Docker to Generate Machine Learning Predictions in Real Time

Figure 1. A REST API serves as the communication layer between a machine learning model and incoming data. Introduction In Part III of our Docker for Machine Learning series, we learned how to use Docker to perform model training and… Read More

Batch Inference vs Online Inference

Introduction You’ve spent the last few weeks training a new machine learning model. After working with the product team to define the business objectives, translating these objectives into appropriate evaluation metrics, and several rounds of iterative feature engineering, you’re ready… Read More

Docker for Machine Learning – Part III

This is Part III of the Docker for Machine Learning series. In Part II of the series we learned how to build custom Docker images and how to use volumes for persisting data in containers. Introduction In Part II of… Read More

Docker for Machine Learning – Part II

This is Part II of the Docker for Machine Learning series. In Part I of the series we learned how to run containers from prebuilt Docker Images. In this post you’ll learn how to build custom images by writing a… Read More

Docker for Machine Learning – Part I

Why is Docker useful? I will admit it, I cannot mention (or even think about) Docker without a large smile coming to my face. Ever since learning about Docker while working as a Data Engineer a few years ago, I’ve… Read More