Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform

Taking a machine learning model from a notebook into a working production service involves more than choosing an algorithm. Pramod Singh’s guide connects core machine learning concepts with the deployment decisions, tools, and operational challenges that arise when models need to run beyond the development environment.

From model fundamentals to deployment decisions

The opening chapters establish useful context with coverage of supervised, unsupervised, semi-supervised, and reinforcement learning, alongside neural networks, cross-validation, and hyperparameters. The book then turns to the practical realities of deployment: model drift, monitoring, release and version management, stakeholder coordination, privacy, and the trade-offs between on-premises and cloud-based systems.

Serve models with Flask and Streamlit

Examples show how machine learning models can be exposed as web services using Flask and Streamlit. The material also includes deep learning deployment, giving readers a view of how model-serving approaches apply across different kinds of workloads.

Package and run models with Docker

A dedicated Docker chapter follows a model deployment workflow from training and exporting a model through building an image and running a container. This makes the relationship between application code, model artifacts, and the container environment part of the practical discussion.

Explore Kubernetes deployment on Google Cloud Platform

The final chapter introduces Kubernetes architecture and applies it to machine learning applications. Together, the Docker and Kubernetes sections show how containerization and orchestration fit into the broader task of running models in production.

For practitioners moving beyond notebooks

Data scientists, data engineers, analysts, and machine learning or deep learning engineers will find a structured introduction to deployment patterns and their operational considerations. The book’s emphasis is not only on frameworks and infrastructure, but also on the challenges that can affect a model after it has been put into service.