Real-time ML-based applications are on the rise but deploying them at scale for large datasets with low latency and high throughput is challenging. This talk discusses the important role of feature stores for machine learning in deploying these applications. By exploring a number of use cases in production, we see how the choice of online data store and the feature store data architecture play important roles in determining its performance and cost. Throughout the presentation, Nava illustrates key points by connecting them to juggling and Dr. Seuss! Stay tuned :)
In this episode
Developer Advocate for Data Science and MLOps, Redis
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 later 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.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.