This block will teach you how to deploy your machine learning models using Docker, Kubernetes, etc.

Model Serialization

Serialization is a technique to convert data structures or object state into a format like JSON, XML, which can later be stored or transmitted and reconstructed.

Updatable Classifiers

This module will teach you how to use updatable classifiers for machine learning models.

Batch mode

Batch mode is a network roundtrip-reduction feature. It is used to batch up data-related operations to perform them in coarse-grained chunks.

Real-time Productionalization (Flask)

In this module, you will learn how to improve your Machine Learning model’s productivity Using Flask.

Docker Containerization – Developmental environment

Docker is one of the most popular tools to create, deploy, and run applications with the help of containers. Using containers, you can package up an application with all the necessary parts like libraries and other dependencies, and ship it all together as one package.

Docker Containerization – Productionalization

In this module, you will learn how to improve the productivity of deploying your Machine Learning models.

Kubernetes

Kubernetes is a tool similar to Docker that is used to manage and handle containers. In this module, you will learn how to deploy your models using Kubernetes.