Practical Full Stack Machine Learning (notice n° 76352)
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| 000 -LEADER | |
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| fixed length control field | 03644cam a2200277zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88939155 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250108000927.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250108s2021 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789391030421 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88939155 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kumar, Alok |
| 245 01 - TITLE STATEMENT | |
| Title | Practical Full Stack Machine Learning |
| Remainder of title | A Guide to Build Reliable, Reusable, and Production-Ready Full Stack ML Solutions |
| Statement of responsibility, etc. | ['Kumar, Alok'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | BPB Publications |
| Date of production, publication, distribution, manufacture, or copyright notice | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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 forThis 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 Contents1. Organizing Your Data Science Project2. Preparing Your Data Structure3. Building Your ML Architecture4. Bye-Bye Scheduler, Welcome Airflow5. Organizing Your Data Science Project Structure6. Feature Store for ML 7. Serving ML as API |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Kumar, Alok |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88939155">https://international.scholarvox.com/netsen/book/88939155</a> |
| Electronic format type | text/html |
| Host name | |
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