Explore the power of feature platforms in AI innovation through this comprehensive MLOps Community Mini Summit. Dive into Tecton's journey in building a unified feature platform for large-scale real-time AI applications using Python, learn about Cleo's production-ready feature platform built with Feast, and discover how to build a feature platform from scratch. Gain insights from industry experts on key architectural tradeoffs, common pitfalls, and best practices in feature engineering. Understand the importance of real-time data processing, predictive features, and model integration in modern AI applications. Learn how Python enables collaboration, rapid iteration, and easy retrieval in enterprise-level AI projects. Explore topics such as subscription models for credit building, flexible feature engineering platforms, and advanced classifier improvements. Discover how compilers process definitions into actionable entities and how to manage state effectively. Gain knowledge about utilizing streaming cases for low-latency data and the use of RocksDB on SSDs for efficient data storage and retrieval.
AI Innovations: The Power of Feature Platforms - MLOps Mini Summit
Overview
Syllabus
[] Real-time data, predictive features, and model integration
[] Fast data-driven model advancement without engineering constraints
[] Supports software team, data scientists' rapid iteration
[] Big organizations use Python for enterprise performance
[] Python enables collaboration, rapid iteration, retrieval ease
[] Subscription model to avoid overdraft, credit building
[]Flexibility in feature engineering platform building process
[] Transformation of features for an advanced classifier, improvements
[] Compiler processes definitions into actionable entities, connectors included
[] Managing and storing state in a compiler
[] Fennel uses rocks DB on SSD's, partitioning, replication
[] Utilizing streaming cases for low-latency data
Taught by
MLOps.community
