SKU/Artículo: AMZ-B0FNLPBVBX

LEARN SPARK ML: Create, Implement, and Master Scalable Machine Learning Pipelines (AI & Machine Learning ENG)

Format:

Paperback

Hardcover

Kindle

Paperback

Detalles del producto
Disponibilidad:
En stock
Peso con empaque:
0.28 kg
Devolución:
Condición
Nuevo
Producto de:
Amazon
Viaja desde
USA

Sobre este producto
  • LEARN SPARK ML:Create, Implement, and Master Scalable Machine Learning PipelinesAimed 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 solutionsAll 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
AR$35.543
31% OFF
AR$24.507

IMPORT EASILY

By purchasing this product you can deduct VAT with your RUT number

AR$35.543
31% OFF
AR$24.507

Pagá fácil y rápido con Mercado Pago o MODO

Llega en 8 a 12 días hábiles
con envío
Tienes garantía de entrega
Este producto viaja de USA a tus manos en