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Built on Open Source

Based on the open-source, industry standards JupyterLab, JupyterHub and Jupyter Notebooks, the PEACH Lab enables data scientist to :

  • Explore data collected by the clients from their local computer without the need to install software and dependencies
  • Create and refine recommendation models
  • Compute business metrics
  • Use common (TensorFlow, scikit-learn, pandas, ...) or specific data science and ML libraries.
  • Share and track code versions using Git
  • Conduct AB testing to measure recommendation performance and improve algorithms

Our additions

We have integrated several additional features on top of Jupyter Lab:

  • Access to Redis, the in-memory data structure store, typically used to store metadata, recommendation models and more
  • Access to Codex, our metadata repository
  • Access to Milvus, the service for a quick vector similarity search
  • Definition of Tasks to automate the computation of code. The typical use case is to create/update recommendation models since we have a constant flow of new users, new user data, and newly published content
  • Automatic deployment of Recommendation API through the use of a simple declarative configuration file
  • Git-based workflow enables data scientists to deploy Tasks and API Recommendation endpoints to production autonomously