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1
Introduction
2
The problem
3
The siloed approach
4
The production approach
5
What do we need to do
6
Problem 1 Silos
7
Operational Pipeline
8
Transformation
9
Serverless
10
Four Big Pillars
11
Feature Store
12
Operations Store
13
Realtime Pipeline
14
CICD Automation
15
ML Project
16
Pipeline Exports
17
ML Run
18
Training
19
Demo
20
Deploy serving function
21
Build topology
22
Mock server
23
Pipeline notebook
24
Pipeline workflow
Description:
Explore Git-based CI/CD for machine learning in this one-hour webinar. Learn how to implement continuous delivery of ML models to production using Git-based pipelines with hosted training and model serving environments. Discover techniques for automating workflows, reviewing models, storing versioned artifacts, and running CI/CD for ML projects. Gain insights into enabling controlled collaboration across ML teams using Git review processes and implementing MLOps solutions with open-source tools and hosted ML platforms. Watch a live demonstration showcasing the practical application of these concepts, covering topics such as deploying serving functions, building topologies, and creating pipeline workflows.

Git-Based CI-CD for ML

Open Data Science
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