Machine Learning with Apache Spark is an intermediate training program designed to help data engineers, machine learning practitioners, and analytics professionals build and deploy scalable ML models using Apache Spark. This course blends foundational ML concepts with hands-on experience in SparkML, enabling you to construct real-world machine learning solutions for large-scale data environments.
What You’ll Learn
Core machine learning concepts and their role in modern data engineering
Overview of generative AI and its relationship to ML workflows
How Apache Spark supports large-scale machine learning and data processing
How to build, evaluate, and deploy ML pipelines using SparkML
Model persistence, optimization, and real-world pipeline structures
Distinguishing between regression, classification, and clustering models
Constructing data analysis processes using Spark SQL
Performing ETL tasks and creating ML models with SparkML and scikit-learn
Course Description
Begin your learning journey with the fundamentals of machine learning before diving into Apache Spark’s powerful distributed computing capabilities. You’ll explore supervised and unsupervised learning methods—such as regression, classification, and clustering—through guided lessons, videos, and readings.
Hands-on labs allow you to:
Use Spark Structured Streaming for real-time data processing
Build data engineering and ML pipelines from ingestion to deployment
Evaluate ML models using SparkML metrics and techniques
Connect to Spark clusters and work with SparkSQL datasets
Perform ETL operations and integrate SparkML with scikit-learn
As you progress, you’ll gain practical experience constructing prediction models, classification systems, and clustering solutions—all designed to help you develop production-ready ML workflows. You’ll conclude the course with a final assignment that demonstrates your applied skills to employers or project teams.
Who This Course Is For
This course is ideal for:
Aspiring and experienced data engineers
Data analysts, BI analysts, and data scientists transitioning into ML
ML practitioners working with large-scale data infrastructures
Professionals seeking hands-on experience with Spark for ML workloads
Prerequisites include:
Familiarity with Big Data concepts, Hadoop, Spark, Python, and ETL processes.