Ray Foundation is a two-day course designed for data professionals who want to scale their machine learning and data workloads. The course covers the full range of the Ray framework, from data processing to building and deploying models. Day one starts with a broad overview before moving into Ray Core, the low-level API for distributed tasks and actors. The day closes with Ray Clusters and the monitoring tools used to keep a deployment healthy.
Day two shifts to the higher-level libraries, covering Ray Data for scalable data loading and preprocessing and Ray Train for distributed model training. It also covers Ray Tune for hyperparameter optimization and Ray Serve for putting trained models into production. You gain hands-on experience each step of the way, building the skills to deploy and administer Ray or work it into your own workflow. Prerequisites include intermediate programming skills, basic familiarity with machine learning, and a willingness to review pre-course materials. By the end, you can use the framework as your go-to distributed computing resource for analytics, data processing, and machine learning at scale.
Who Should Attend
Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals
What Attendees Will Learn
Upon completing Ray Foundation, participants will be able to:
- Deploy and utilize Ray for scalable machine learning solutions
- Develop experience using Ray to perform computations on large datasets
- Acquire hands-on experience building and serving machine learning models at scale
- Utilize a wide array of hyperparameter optimization libraries to perform distributed tuning machine learning models
- Gain the skills and understanding necessary deploy Ray clusters and utilize them for analytics, data processing and machine learning at scale
Prerequisites
Participants should have intermediate programming skills (preferably in Python), some understanding of mathematics and statistics, basic familiarity with machine learning, a grasp of computer science fundamentals, familiarity with common operating systems and basic command-line operations, and a willingness to review pre-course materials.
These prerequisites will help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding.