LEARN SPARK ML: Create, Implement, and Master Scalable Machine Learning Pipelines

Aimed at students, professionals, and data enthusiasts who want to learn, implement, and automate machine learning pipelines using Spark ML in real-world environments. This book teaches everything from data ingestion to model deployment in production, with hands-on integration of leading market services, including AWS, Azure, Google Cloud, Databricks, Hadoop, Kubernetes, Apache Airflow, S3, BigQuery, Redshift, and Delta Lake.

The content covers:

- Integration of Spark ML with cloud environments and data platforms

- Construction and automation of pipelines with Spark MLlib and Airflow

- Implementation of supervised and unsupervised models

- Deployment, monitoring, and management of models in cloud and hybrid environments

- Workflow optimization with Delta Lake, BigQuery, and Redshift

- Tuning techniques, cross-validation, and MLOps fundamentals

- Performance analysis and scalability of machine learning solutions

All examples and routines serve as a starting point, allowing adaptation to different academic and professional contexts. The goal is to deliver technical onboarding, practical autonomy, and mastery of the most widely used integrations in the market.

spark ml, aws, azure, google cloud, databricks, hadoop, airflow, s3, bigquery, redshift, delta lake, pipelines, mlops, deploy, automation, predictive models