Redis for AI and ML on JupyterHub packaged by Data Science Dojo
Data Science Dojo
Redis for AI and ML on JupyterHub packaged by Data Science Dojo
Data Science Dojo
Redis for AI and ML on JupyterHub packaged by Data Science Dojo
Data Science Dojo
Jupyter notebook with Redis, Redis-py, RedisJSON and RedisML
Data Science Dojo's mission is to make data science easier, more practical, and accessible to everyone.
Trademarks: This software listing is packaged by Data Science Dojo. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.
About the offer:
This offer will help you scale your ML infrastructure almost instantly. It's a JupyterHub for up to 100 users. It comes pre-loaded with Python and Redis. Redis is a powerful real-time data platform.
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions, and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster.
Popular Redis use cases include:
- Enterprise caching
- Session management
- Leaderboards
- Real-time inventory
- Fraud mitigation
- Claims processing
Who benefits from this offer:
This offer is designed for developers who want to realize the power of real-time ML with sub-millisecond speed at an infinite scale.
- Great for ML on mobile
- Quick and easy infrastructure setup
- Pre-installed packages and libraries to get started quick
What's included in this offer: This image contains the following, all pre-installed for your convenience:
Redis
- Redis library for Python
- Redis Server
- RedisJSON
- Redis ML
JupyterHub
- Built on the Littlest JupyterHub, allowing 1-100 users on a single machine.
Python 3.7
- Python Libraries: numpy, scipy, matplotlib, pandas, scikit-learn, seaborn, beautifulsoup4, plotly, opencv-python, azure-storage-blob, azure-storage-file, azure-storage-queue, azure-storage-common
Technical Specifications:
- Minimum requirements: 2 CPU, 4 GB Memory
- Operating System: "Ubuntu 18.04"
- Authoring Tools: Jupyter, Jupyter Lab, and Terminal
- Users can access the VM via Remote Desktop, SSH, or browser