Practical Full Stack Machine Learning by Alok Kumar [Paperback]

Become a Full-Stack Machine Learning Pro with "Practical Full Stack Machine Learning" by Alok Kumar

Take your machine learning (ML) skills to the next level with "Practical Full Stack Machine Learning" by Alok Kumar. This book equips aspiring data scientists with the knowledge and tools to design, build, and deploy production-ready ML applications, guiding you through the entire machine learning lifecycle from start to finish.

Key Features:

Master the entire ML pipeline: Learn how to construct reusable ML pipelines that seamlessly transition from development to production environments.

Optimize for efficiency and performance: Discover step-by-step strategies to tackle every data science task with maximum efficiency and achieve peak performance in your ML models.

Leverage cutting-edge tools: Gain insights into advanced data engineering and ML tools like Airflow, MLflow, and ensemble techniques, empowering you to streamline your workflow.
What You Will Learn:

Craft production-ready ML pipelines: This book dives deep into building reusable ML pipelines that can be effortlessly integrated into production environments.
Scale your data pre-processing: Implement scalable solutions for data pre-processing tasks using DASK, a powerful parallel computing library.

Harness the power of ensembles: Experiment with effective ensemble learning techniques like Bagging, Stacking, and Boosting to enhance the accuracy and robustness of your models.

Automate data preparation: Explore Airflow, a workflow management framework, to automate your ETL (Extract, Transform, Load) tasks and streamline data preparation.
Deploy and manage models with MLflow: Learn how to leverage MLflow, a powerful tool for managing the entire ML lifecycle, including training, reusing, and deploying models built with any library.

Master a comprehensive toolkit: This book delves into various industry-standard tools like Cookiecutter for project templating, KerasTuner for hyperparameter tuning, DVC for version control, and FastAPI for building high-performance APIs to serve your ML models.

Who This Book Is For:

Data scientists seeking to elevate their skills: This book is designed for data scientists who want to transition from building basic ML models to developing and deploying full-fledged production-ready ML applications.

Prior knowledge assumed: A foundational understanding of machine learning concepts and Keras programming is recommended to get the most out of this book.
By the time you've finished "Practical Full Stack Machine Learning," you'll be equipped to:

Confidently design and implement the entire ML pipeline for real-world applications.
Construct scalable data pre-processing workflows using DASK.

Build robust and accurate models using ensemble learning techniques.
Automate data preparation tasks with Airflow.
Deploy and manage ML models effectively with MLflow.
Utilize a comprehensive set of industry-standard tools to streamline your ML development process.

Become a well-rounded machine learning expert. Get your copy of "Practical Full Stack Machine Learning" today!