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YouTube

Production Model Deployment

GOTO Conferences via YouTube

Overview

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Explore the intricacies of deploying production models in this 46-minute conference talk from GOTO Chicago 2018. Gain insights from Juliet Hougland, a Data Platform & ML Engineer at Stitchfix, as she delves into the various aspects of model deployment. Learn about different deployment types, including data, API, and code artifact deployments, and how they fit into model development workflows. Discover the challenges of transmitting model outputs to other systems reliably and the importance of feature stores. Understand the concept of AB testing and its role in model evaluation. Examine the engineering requirements for machine learning pipelines, including lambda architecture and model throughput considerations. Explore the dynamics between data scientists and software engineers, and learn strategies for effective communication and handoffs in model deployment. Gain valuable knowledge on topics such as serialization, PMML, and its limitations. This comprehensive talk covers team structures, QA processes, and addresses general questions about production model deployment, providing a holistic view of the subject for data scientists and engineers alike.

Syllabus

Intro
Juliets background
Agenda
Lifecycle
Black boxes
Featureization
Deployment as Sharing
Building a Service
Models
Model App
Problem Serialization
Does it Function
Does it Work
AB Testing
AB Testing Forever
Deployment
Deployment schedule
When to deploy
Machine learning pipelines
Lambda architecture
Engineering requirements
Model throughput
Feature store
Model output
Evergreen solution
The handoff
Conways Law
Data Scientist vs Software Engineer
Data Science Departments
Machine Learning
Communication
Model Handoff
Clean Interfaces
Serialization
PMML
Limitations of PMML
General Questions
Team Structure
QA

Taught by

GOTO Conferences

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