When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
This Spark and Machine Learning training teaches participants how to build, deploy, and maintain powerful data-driven solutions using Spark and its associated technologies. The course begins with an...Read More
This Spark and Machine Learning training teaches participants how to build, deploy, and maintain powerful data-driven solutions using Spark and its associated technologies. The course begins with an introduction to Spark, its architecture, and how it fits into the Hadoop and Cloud-based ecosystems. Participants will learn to set up Spark environments using DataBricks Cloud, AWS EMR clusters, and SageMaker Studio. In addition, students will learn about Spark's core functionalities, including RDDs, DataFrames, transformations, and actions.
This course targets data scientists, machine learning engineers, big data engineers, and other professionals with experience in data analysis who wish to leverage Spark for scalable machine learning solutions. It is also suitable for those who want to enhance their large-scale data processing and machine learning knowledge.
Chapter 1 - Introduction to Spark. Overview of Spark and its Architecture
Chapter 2 - Introduction to Spark - Setting up a Spark Environment
Chapter 3 - Basic Spark Operations and Transformations
Chapter 4 - Introduction to Spark SQL
Chapter 5 - Spark's ML libraries - Lecture: Introduction to Spark's ML libraries
Chapter 6 - Streaming and Graphs
Chapter 7 - Deploying Spark ML Artifacts - Introduction to deploying Spark ML Artifacts
Chapter 8 - Machine learning at Scale - Introduction to Machine Learning at Scale
Chapter 9 - Machine learning at Scale - Distributed Training of Machine Learning models
Chapter 10 - Machine learning at Scale - Hyperparameter tuning and model selection at scale