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Dataiku
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Dataiku
by Clément Stenac, Léo Dreyfus-Schmidt, Kenji Lefevre,
Introducing MLOps
Dataiku
1. Why Now and Challenges
Defining MLOps and Its Challenges
MLOps to Mitigate Risk
MLOps for Responsible AI
MLOps for Scale
Closing Thoughts
2. People of Model Ops
Subject Matter Experts
Role in the Machine Learning Model Lifecycle
Role In and Needs From MLOps
Data Scientists
Role in the Machine Learning Model Lifecycle
Role In and Needs From MLOps
Data Engineers
Role in the Machine Learning Model Lifecycle
Role In and Needs From MLOps
Software Engineers
Role in the Machine Learning Model Lifecycle
Role In and Needs From MLOps
DevOps
Role in the Machine Learning Model Lifecycle
Role In and Needs From MLOps
Model Risk Manager/Auditor
Role in the Machine Learning Model Lifecycle
Role In and Needs From MLOps
Machine Learning Architect
Role in the Machine Learning Model Lifecycle
Role In and Needs From MLOps
Closing Thoughts
3. Key MLOps Features
A Primer on Machine Learning
Different ML Algorithms, Different MLOps Challenges
Model Development
Establishing Business Objectives
Data Sources and Exploratory Data Analysis
Feature Selection and Engineering
Training
Additional Considerations for Model Development
Productionalization and Deployment
Model Deployment Types and Contents
Model Deployment Requirements
Monitoring
DevOps Concerns
Data Scientist Concerns
Business Concerns
Iteration and Lifecycle
Iteration
The Feedback Loop
Governance
Governance and MLOps
Closing Thoughts
4. Monitoring and Feedback Loop
How Often Should Models Be Retrained?
Understanding Model Degradation
Ground Truth Evaluation
Input Drift Detection
Drift Detection in Practice
Example Causes of Data Drift
Input Drift Detection Techniques
The Feedback Loop
Logging
Model Evaluation Store
Online Evaluation
Closing Thoughts
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Introducing MLOps
Dataiku
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