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CODE: NAI13
DURATION: 3 Days | 5 Days | 10 Days
CERTIFICATIONS: CPD
This course provides a comprehensive introduction to building scalable machine learning solutions using Apache Spark and its MLlib library. Participants will learn how to process large datasets, develop distributed machine learning models, and optimize workflows for performance across clusters. The course is a combination of theory with practical training to help participants apply Spark’s APIs in Python or Scala, enabling scalable and efficient AI workflows for big data environments.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Know the fundamentals of distributed computing with Apache Spark.
Build and train scalable machine learning models using Mllib.
Preprocess and transform large datasets for model training.
Apply classification, regression, clustering, and recommendation techniques.
Optimize ML workflows and manage model persistence.
Use Spark ML pipelines for automation and reproducibility.
Integrate Spark with cloud platforms and data lakes.
Troubleshoot performance issues and tune distributed systems.
At the end of this course, you’ll understand:
This course is designed for data engineers, machine learning engineers, data scientists, and technical professionals working with large-scale data. It is also important for software developers and architects looking to build distributed AI systems using Apache Spark. Familiarity with Python or Scala and basic machine learning concepts is recommended.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
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