Embrace DevOps Practices to ML Pipeline Development
Offered By: CNCF [Cloud Native Computing Foundation] via YouTube
Course Description
Overview
Explore DevOps practices for machine learning pipeline development in this 22-minute conference talk from KubeCon + CloudNativeCon North America 2021. Discover how IBM's Tommy Li and Yihong Wang integrate DevOps methodologies into ML workflows using Kubeflow Pipelines with Tekton. Learn about consolidating end-to-end ML scenarios, building deployment status dashboards, and implementing Slack channel notifications. Gain insights into overcoming challenges in the CI/CD process for Kubernetes clusters, and explore tools and services that facilitate seamless integration of ML pipelines into DevOps practices. Dive into topics such as ML lifecycle, Q4 Pipeline, Python SDK, artifact tracking, and the IBM Toolchain for efficient CICD pipeline implementation.
Syllabus
Introduction
Agenda
ML Lifecycle
Q4 Pipeline
Python SDK
Q4 Pipelines
Track artifacts
Replicate public experience
Scope
ML pipelines
IBM Toolchain
CICD Pipeline
Demo Process
Challenges
Resources
Taught by
CNCF [Cloud Native Computing Foundation]
Related Courses
Building End-to-end Machine Learning Workflows with KubeflowPluralsight 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