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World Congress 2024 • Aug 20, 2024 • Session details

The Road to MLOps: How Verivox Transitioned to AWS

Elisabeth Günther

Struggling with siloed spaghetti code, Verivox pivoted to a scalable AWS-native MLOps architecture. Discover how they slashed model deployment from months to hours and rapidly unlocked generative AI.

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#1 about 1 min

Introduction to the Verivox business and services

An overview of the company's online comparison portal and its mission to save users time and money.

#2 about 2 min

The lifecycle and challenges of machine learning projects

The standard phases of building a machine learning model highlight the need for consistent deployment processes.

#3 about 3 min

Understanding the intersection of data science and operations

Integrating multiple disciplines is necessary to productionize machine learning solutions efficiently.

#4 about 4 min

Defining the four phases of machine learning operations maturity

A sequential guide helps teams move from initial proof of concept to scalable multi-project deployment.

#5 about 3 min

Challenges with legacy operational models and siloed codebases

Transitioning away from fragmented notebook deployments requires overcoming significant bottlenecks and missing cross-team responsibilities.

#6 about 4 min

Rebuilding workflows with basic cloud and automation milestones

Shifting from on-premises to the cloud involves standardizing on Python, introducing continuous deployment, and eliminating manual configuration.

#7 about 4 min

Designing an automated machine learning deployment blueprint

Using managed pipelines and custom deployment templates dramatically reduces the time required to push live inference APIs into production.

#8 about 2 min

Managing legacy workflows using containerization and serverless orchestration

Flexible architectures process batch computing jobs and custom models securely using fully managed container services.

#9 about 5 min

Deep dive into infrastructure as code with deployment kits

Defining scalable infrastructure via code templates enables rapid instantiation of isolated environments across multiple projects and stages.

#10 about 4 min

Key learnings and outcomes from the cloud modernization journey

Building strong foundational architectures shrinks deployment timelines from months to hours and unlocks rapid experimentation with emerging technologies.

Matching moments

5:28 min

Defining MLOps and its role in production systems

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Real-world application of MLOps architectural patterns at Wayfair

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4:19 min

Introduction to DevOps for AI and MLOps

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3:56 min

Solving application deployment complexities using LLMOps pipelines

Anshul Jindal Anshul Jindal · World Congress 2025

3:40 min

Navigating the MLOps tooling landscape and vendor decisions

Bas Geerdink · LIVE

2:14 min

Differences between traditional MLOps and GenAIOps

Maxim Salnikov Maxim Salnikov · World Congress 2025

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