Launch, scale, and optimize distributed AI workloads on Anyscale with hands-on support from Forward Deployed Engineers and expert-led training.
The fastest way to launch and scale workloads on Anyscale with hands-on engineers who:
Applied expertise on distributed systems for data, train and serve workloads.
Your team leaves with reusable patterns and the ability to operate independently.
Ready to help you launch to production, implement best practices to onboard multiple teams or optimize workload performance at scale.
Benefits of FDEs for Ray on Anyscale
Stand up Anyscale with the right cluster, storage, and scaling configs. Migrate existing workloads or implement new production-ready pipelines with Ray Data, Train, Serve.
Scale training, tuning, and inference workloads. Apply proven Ray patterns for parallelism and orchestration. Standardize how teams build on Anyscale.
Debug distributed bottlenecks across data, training, and serving. Optimize autoscaling, resource utilization, and cost. Improve fault tolerance and production reliability
Enablement is designed to complement FDE engagements so your team can both accelerate adoption and take ownership of what gets built.
Tailored to your workloads and teams with curriculum aligned to your use case (LLMs, RecSys, Robotics, etc). Delivered live (virtual or on-site) with hands-on labs using your environment
Learn the foundation of distributed computing with Ray with workload based modules (data, train, serve) using practical, production-oriented examples
Build practical experience and validate your skills through our evolving certification and accreditation programs designed around real distributed workloads.