Explore real-time stream processing for machine learning in this 28-minute talk by Alejandro Saucedo from The Institute for Ethical AI & Machine Learning. Dive into the evolution of ETL processes, compare batch vs streaming approaches, and understand key streaming concepts like windows, checkpoints, and watermans. Learn about machine learning workflows, model pipeline components, and feature engineering. Examine architecture overviews for Kubernetes, discussing horizontal and vertical scalability. Gain insights into native service orchestration integration beyond REST and API protocols. Access code examples and relevant links to enhance your understanding of implementing machine learning in real-time streaming environments.
Hands-On Real Time Stream Processing for Machine Learning
Overview
Syllabus
Intro
Hello, my name is Alejandro
The Institute for Ethical Al & Machine Learning
We are part of the LFAI
Real Time Reddit Processing
A trip to the past present: ETL
Variations
Specialised Tools
Batch VS Streaming
Unifying Worlds Stream (continuously arriving data)
Streaming Concepts: Windows
Streaming Concepts: Checkpoin
Streaming Concepts: Waterman
Machine Learning Workflow
Today we're using
Model Pipeline Components
Features
More on EDA & Model Evaluation
Architecture Overview Kubernetes
Horizontal & Vertical Scalability Native service orchestration integration besides REST & API protocols
Code Examples & Links
Taught by
Linux Foundation
Tags
Related Courses
-
Real Time Stream Processing for Machine Learning at Massive Scale
-
Enable Real-time Analytics and Machine Learning with Azure Stream
-
Fast Data Architectures for Streaming Applications
-
Optimal Approaches for Real-Time Machine Learning with Apache Spark on Kubernetes
-
Building a Real-Time Feature Store for Machine Learning at iFood
-
Real-time Trade Data Processing and Machine Learning at Giga-Scale with Hazelcast and FINOS Perspective
