About the Course
Apache SystemML is a declarative style language designed for large-scale machine learning. It provides automatic generation of optimized runtime plans ranging from single-node, to in-memory, to distributed computations on Apache Hadoop and Apache Spark. SystemML algorithms are expressed in R-like or Python-like syntax that includes linear algebra primitives, statistical functions and ML-specific constructs.
As a data scientist, engineer, or just a fellow interested in machine learning, your productivity will increase while having the flexibility to express custom analytics and not worry about the underlying optimization engine. Automatic scalability and optimization is handled by SystemML.
This course will not only provide you with a view of how the optimizers function but also with hands-on examples of ML algorithms and how to run them.
- Purpose and origin of SystemML
- Alternatives to SystemML
- Performances of SystemML and the alternatives
- MLContext to interact with SystemML (in Scala)
- Various SystemML algorithms
- Purpose of DML
- The DML language
- Built-in functions
- The optimizer stack
- SystemML know it's better to run on one machine
- SystemML is much faster than single-node R
- Flexible enrolment
- Audit multiple times
Recommended Existing Skills
- The Big Data Fundamentals learning path.
- The Hadoop Fundamentals learning path.
- Basic understanding of Apache Hadoop and Big Data.
- Basic Linux Operating System knowledge
Henry L. Quach
Henry L. Quach is the Technical Curriculum Developer Lead for Big Data. He has been with IBM for 9 years focusing on education development. Henry likes to dabble in a number of things, including being part of the original team that developed and designed the concept for the IBM Open Badges program. He has a Bachelor of Science in Computer Science and a Master of Science in Software Engineering from San Jose State University.