The Scalable Machine Learning (SML) course is designed and developed to provide students with exposure in Scalable Machine learning. The course focuses on utilizing the Hadoop and Spark Frameworks to implement SML Algorithms via Scala and Python programming languages.
The course begins with an introduction to SML and why developers use Spark for SML Next, the course dives into data acquisition, data pre-processing for modeling, and working with Iterative algorithms. The course concludes with model evaluation, optimization and deployment.
Purpose
|
Learn about and build end-to-end SML pipelines for gaining actionable insights. |
Audience
|
Teams needing to gracefully scale up their Machine Learning projects. |
Role
| Data Engineer - Data Scientist - Software Developer |
Skill Level
| Intermediate |
Style
| Hack-a-thon - Learning Spikes - Workshops |
Duration
| 3 Days |
Related Technologies
| Apache Spark | Hadoop | Python |
Productivity Objectives
- Describe the role of Spark in Machine Learning.
- Apply Machine learning on massive datasets.
- Demonstrate experience in Data Acquisition, Processing, Analysis and Modeling using Hadoop and Spark.
- Evaluate various common types of data e.g. CSV, XML, JSON, Social Media data, etc. for pre-processing and/or building Machine Learning Models using Spark.
- Train, tune, test and deploy Machine Learning Models.