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Master the ML process, from pipeline development to model deployment in production.n nKEY FEATURES n¿ Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API.n¿ A step-by-step approach to cover every data science task with utmost efficiency and highest performance.n¿ Access to advanced data engineering and ML tools like AirFlow, MLflow, and ensemble techniques.n nWHAT YOU WILL LEARNn¿ Learn how to create reusable machine learning pipelines that are ready for production.n¿ Implement scalable solutions for pre-processing data tasks using DASK.n¿ Experiment with ensembling techniques like Bagging, Stacking, and Boosting methods.n¿ Learn how to use Airflow to automate your ETL tasks for data preparation.n¿ Learn MLflow for training, reprocessing, and deployment of models created with any library.n¿ Workaround cookiecutter, KerasTuner, DVC, fastAPI, and a lot more.nnnWHO THIS BOOK IS FOR nThis 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.n nTABLE OF CONTENTSn1. Organizing Your Data Science Projectn2. Preparing Your Data Structuren3. Building Your ML Architecturen4. Bye-Bye Scheduler, Welcome Airflown5. Organizing Your Data Science Project Structuren6. Feature Store for ML n7. Serving ML as API
