Practical Full Stack Machine Learning : A Guide to Build Reliable, Reusable, and Production-ready Full Stack Ml Solutions

Master the ML process, from pipeline development to model deployment in production.

Key Features

● Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API.

● A step-by-step approach to cover every data science task with utmost efficiency and highest performance.

● Access to advanced data engineering and ML tools like AirFlow, MLflow, and ensemble techniques.

Description

'Practical Full-Stack Machine Learning' introduces data professionals to a set of powerful, open-source tools and concepts required to build a complete data science project. This book is written in Python, and the ML solutions are language-neutral and can be applied to various software languages and concepts.

The book covers data pre-processing, feature management, selecting the best algorithm, model performance optimization, exposing ML models as API endpoints, and scaling ML API. It helps you learn how to use cookiecutter to create reusable project structures and templates. It explains DVC so that you can implement it and reap the same benefits in ML projects.It also covers DASK and how to use it to create scalable solutions for pre-processing data tasks. KerasTuner, an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search will be covered in this book. It explains ensemble techniques such as bagging, stacking, and boosting methods and the ML-ensemble framework to easily and effectively implement ensemble learning.

The book also covers how to use Airflow to automate your ETL tasks for data preparation. It explores MLflow, which allows you to train, reuse, and deploy models created with any library. It teaches how to use fastAPI to expose and scale ML models as API endpoints.

What you will learn

● Learn how to create reusable machine learning pipelines that are ready for production.

● Implement scalable solutions for pre-processing data tasks using DASK.

● Experiment with ensembling techniques like Bagging, Stacking, and Boosting methods.

● Learn how to use Airflow to automate your ETL tasks for data preparation.

● Learn MLflow for training, reprocessing, and deployment of models created with any library.

● Workaround cookiecutter, KerasTuner, DVC, fastAPI, and a lot more.

Who this book is for

This book is geared toward data scientists who want to become more proficient in the entire process of developing ML applications from start to finish. Knowing the fundamentals of machine learning and Keras programming would be an essential requirement.

Table of Contents

1. Organizing Your Data Science Project

2. Preparing Your Data Structure

3. Building Your ML Architecture

4. Bye-Bye Scheduler, Welcome Airflow

5. Organizing Your Data Science Project Structure

6. Feature Store for ML

7. Serving ML as API