YoVDO

Using Model Serving in Streaming Applications

Offered By: Toronto Machine Learning Series (TMLS) via YouTube

Tags

Machine Learning Courses Data Science Courses Kubernetes Courses Kubeflow Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the integration of machine learning models in streaming applications with this insightful conference talk by Boris Lublinsky, Principal Architect at Lightbend. Delve into the shift from batch to real-time streaming and its impact on user expectations. Discover how machine learning enhances mission-critical real-time applications, enabling innovative solutions and improving traditional scenarios like fraud detection and predictive maintenance. Learn about two main approaches to model usage in streaming applications: embedded models and external servers. Gain hands-on knowledge of Cloudflow, a specialized streaming framework for developing, orchestrating, and operating distributed streaming applications on Kubernetes. Understand how Cloudflow supports the entire application lifecycle, from development to operation. Examine the implementation of different model serving approaches using Cloudflow and its integration with Kubeflow. Conclude with valuable recommendations for choosing the most suitable model serving approach based on specific requirements.

Syllabus

Boris Lublinsky - Using Model Serving in Streaming Applications


Taught by

Toronto Machine Learning Series (TMLS)

Related Courses

Building End-to-end Machine Learning Workflows with Kubeflow
Pluralsight
Smart Analytics, Machine Learning, and AI on GCP
Pluralsight
Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications
LinkedIn Learning
Distributed TensorFlow - TensorFlow at O'Reilly AI Conference, San Francisco '18
TensorFlow via YouTube
KFServing - Model Monitoring with Apache Spark and Feature Store
Databricks via YouTube