This course lays out the common architecture, infrastructure, and theoretical considerations for managing an enterprise machine learning (ML) model pipeline. Because automation is the key to effective operations, you'll learn about open source tools like Spark, Hive, ModelDB, and Docker and how they're used to bridge the gap between individual models and a reproducible pipeline. You'll also learn how effective data teams operate; why they use a common process for building, training, deploying, and maintaining ML models; and how they're able to seamlessly push models into production. The course is designed for the data engineer transitioning to the cloud and for the data scientist ready to use model deployment pipelines that are reproducible and automated. Learners should have basic familiarity with: cloud platforms like Amazon Web Services; Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; Bash, Docker, and REST.
Jason Slepicka is a senior data engineer with Los Angeles based DataScience, where he builds pipelines and data science platform infrastructure. He has a decade of experience integrating data to support efforts like fighting human trafficking for DARPA, exploring the evolution of evolvability in yeast, and tracking intruders in computer networks. Jason has both a Bachelor's and Master’s in Computer Science from the University of Arizona and is working on his PhD in Computer Science at the University of Southern California Information Sciences Institute.