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RISE Camp ​is ​a bootcamp ​organized ​by ​the ​UC ​Berkeley ​RISELab ​where ​you ​can get exposure to research and ​hands-on ​experience ​with systems and technologies for ​emerging AI applications including reinforcement learning, prediction serving, agile ML development, context management, and AI security. ​Please check this page for all updates on ​the ​curriculum, ​slides, ​links ​to ​resources, ​and ​more. PLEASE NOTE: RISE Camp attendance is by invite only, but will be live-streamed for free.

LIVE STREAMING LINK can be found HERE .

Agenda

Agenda

DAY 1 – THURSDAY, SEPTEMBER 7, 2017

Theme: Systems support for Emerging AI applications

  • 8:00AM: Breakfast
  • 9:00AM: Lab Introduction/Overview (Ion Stoica) 
  • 9:15AM: Project Introductions/Overview Talk (Alexey Tumanov) 
  • 9:30AM: RL Concepts Talk
  • 10:30­AM: Break
  • 11:00AM: Ray: Distributed Execution Framework for Emerging AI Applications

    • 11:00AM: Ray Talk 
    • 11:30AM: Ray Tutorial Lab 
  • 1:00PM: Lunch
  • 2:30PM: Clipper: a Low-Latency Online Prediction Serving System
  • 4:00­PM: Break
  • 4:30PM: Integrated and Advanced Exercises (Capstone lab)
    • 4:30PM: Ray Reinforcement Learning (RL) library tutorial
    • 5:00PM: Pong game – trained in Ray, Clipper served 
          • Train the pong game policy with Ray
          • Use Clipper to deploy the trained policy
          • Beat the policy you’ve just trained
          • Enjoy the game of pong!
    • 6:00­ – 8:30PM: Reception

DAY 2 – FRIDAY, SEPTEMBER 8, 2017

Theme: Data Analytics and Security

  • 8:00AM: Breakfast
  • 9:00AM: PyWren: Pushing Microservices to Teraflops

    • 9:00AM: PyWren Talk
    • 9:30AM: PyWren Lab
    • 10:30AM: Break
  • 11:00AM : Ground: An open-source data context service

  • 12:30 ­- 2:00PM: Lunch
  • 2:00­PM: WAVE: Global-scale Authorization for IoT without an Authority

    • 2:00­PM: WAVE talk
    • 2­:30: WAVE Tutorial lab
  • 3:30 – ­3:45PM Concluding remarks (Raluca Popa)

 

FAQs

FAQs

What are the prerequisites for attendees of the RISE Camp?

  • Experience programming in Python in notebook environment
  • Basic understanding of AI/ML concepts (e.g., training, validation, linear models)
  • Laptop (with charger) and “modern” browser (Firefox, Chrome, Safari etc. – for technical details, please go to this link)