As data scientists and machine learning professionals make the transition from theory to applied data science, they naturally expand their skill set beyond Python or Pandas. They need to understand how to leverage a key-value store or database to store their embeddings or features, and how to load and fetch them very quickly for online predictions or to perform complex operations in milliseconds for real-time use cases. Because of that, Redis — the super-fast in-memory database, is being increasingly used for machine learning: from caching, messaging, and fast data ingestion, to vector similarity search and online feature stores.
This crash course is for ML and Data Engineers looking to learn more about deploying real-time AI/ML at scale, as well as Data Scientists making the transition from theory to applied Data Science. It assumes no prior experience in Redis.
- What a key-value store is, and the difference between Redis and SQL databases
- Which key machine learning concepts and use cases Redis enables
- Which data types and structures can be stored in Redis
- Key database considerations for deploying real-time AI/ML at scale
- Redis as an online feature store - why and how to get started
- Redis as a vector database for embeddings and neural search - why and how to get started
Taimur Rashid is the Chief Business Officer at Redis where he focuses on AI/ML and is a Data Science Dojo Instructor. He is passionate about using emerging technologies to create new markets and products. Having spent the last three years leading solution architecture for Microsoft Azure Data and AI, and then 10 years prior to that leading market development and new product incubation for AWS, he has a vast amount of experience bringing new products to market. Taimur has a bachelor’s in computer science from the University of Texas at Austin where he focused on Automata Theory and Knowledge-Based Systems. He is a frequent instructor on innovation, business strategy, and market development.
Nava is a Developer Advocate for Data Science and MLOps at Redis. She started her career in tech with an R&D Unit in the IDF and had the good fortune to work with and champion Cloud, Big Data, and DL/ML/AI technologies just as the wave of each of these was starting. Nava is also a mentor at the MassChallenge accelerator and the founder of LerGO—a cloud-based EdTech venture. In her free time, she enjoys cycling, 4-ball juggling, and reading fantasy and sci-fi books.