PRACTICAL FULL STACK MACHINE LEARNING IBD

Sinopse

Master the ML process, from pipeline development to model deployment in production.áKEY FEATURESááâùÅ Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API.âùÅ A step-by-step approach to cover every data science task with utmost efficiency and highest performance.âùÅ Access to advanced data engineering and ML tools like AirFlow, MLflow, and ensemble techniques.áWHAT YOU WILL LEARNâùÅ Learn how to create reusable machine learning pipelines that are ready for production.âùÅ Implement scalable solutions for pre-processing data tasks using DASK.âùÅ Experiment with ensembling techniques like Bagging, Stacking, and Boosting methods.âùÅ Learn how to use Airflow to automate your ETL tasks for data preparation.âùÅ Learn MLflow for training, reprocessing, and deployment of models created with any library.âùÅ Workaround cookiecutter, KerasTuner, DVC, fastAPI, and a lot more.WHO THIS BOOK IS FORááThis 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.áTABLE 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 MLá7. Serving ML as API