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Data Scientist Platform Release Notes

26 August 2019

  • Restructure technical documentation
  • Documentation about Git workflow and PEACH Lab
  • Project template for new projects in PEACH Lab

29 July 2019

  • PEACH Lab and Git workflow ready for production
  • Python 3 kernel is available in PEACH Lab

31 May 2019

  • Recommendation API supports Git Workflow

25 Apr 2019

  • Tasks scheduler supports Git Workflow

29 Mar 2019

  • GitLab integration for PEACH Lab

11 Mar 2019

  • PEACH Lab is available in beta

10 August 2018

  • Realtime user play history
    • Main use case : filter out recently watched content from recommendation
    • Last 24 hours of media consumption by user stored in redis in realtime including anonymous users
    • Enabled only for broadcasters running PEACH in production at the moment
    • History can be retrieved in the endpoints in the following way:
      from pipe_algorithms_lib.history_utils import realtime_history
# list of media ids ordered by time from old to new ones using pipe_c cookie
history = realtime_history(codops, cookie_id)

8 March 2018

  • List HDFS files from the Notebooks

    from pipe_algorithms_lib.hadoop import ls

# Basic
ls(path="/")

# With a defined path
ls(path="/recsys/chrts/realtime")

# List with all details
ls(path="/recsys/chrts/realtime", all=True)

  • Custom Python environment for Notebooks & Tasks

    • This allows data scientist to use third-party libraries in their code seamlessly on the platform.
    • Custom Python environment for endpoints was already provided some weeks ago.
    • Data scientist can also leverage this to create their own libraries to factor common code, thus improving maintenance, reusability and sharing across broadcasters and teams.
    • Common libraries can also be versioned in Git (using EBU GitLab or public Github repositories)
    • How does it work?