Course Code: ML0111EN
Audience: Data Scientist, Data Engineer
Course Level: Intermediate
Time to Complete: 8 hours
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.
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.