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World Congress 2022 • Jun 15, 2022

MLOps - What’s the deal behind it?

Nico Axtmann

Why do 90% of AI initiatives fail? Pure data science isn't enough anymore. Discover how MLOps and software engineering fundamentals overcome hidden technical debt to deploy real-world products.

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#1 about 1 min

Creating automated generative media and artificial intelligence products

An overview of practical applications for big language models and synthetic media generation.

#2 about 3 min

Major breakthroughs shaping the artificial intelligence landscape

How deep neural networks and transformer models drove rapid advancements in natural language processing.

#3 about 3 min

The gap between research benchmarks and industry application

Why focusing on perfectly crafted datasets and isolated benchmarks creates unrealistic expectations for business applications.

#4 about 2 min

Why corporate machine learning initiatives fail to scale

The immense engineering effort required causes most companies to miscalculate the difficulty of deploying models.

#5 about 2 min

Uncovering the hidden technical debt in machine learning

How serving infrastructure and complex dependencies dwarf the actual machine learning code in production systems.

#6 about 2 min

The exponential growth and hype of the MLOps market

The rapid expansion of startups addressing niche needs within the emerging machine learning operations space.

#7 about 3 min

Defining machine learning operations in a fragmented ecosystem

Exploring standardized definitions for deploying, tuning, and ensuring reproducibility of models across diverse data landscapes.

#8 about 3 min

Solving complex engineering challenges in artificial intelligence deployment

Managing the chaotic side effects between interdependent data processing, infrastructure changes, and model training loops.

#9 about 3 min

Navigating tooling fragmentation and vendor lock-in risks

Strategies for integrating diverse technology stacks without becoming dependent on unmaintained or proprietary enterprise frameworks.

#10 about 3 min

Leveraging open source frameworks for reproducible model deployments

How standardized open source environments and data versioning prevent unstable code deployment in production.

#11 about 3 min

Why machine learning engineering replaces data science hype

Strong software engineering fundamentals are essential to bridging the gap between research concepts and stable systems.

#12 about 7 min

Evaluating frameworks, open source tools, and role definitions

Practical insights into the distinction between engineering and operations, model serialization, and identifying foundational career paths.

Matching moments

5:28 min

Defining MLOps and its role in production systems

Hauke Brammer · WWC 2023

4:19 min

Introduction to DevOps for AI and MLOps

Aarno Aukia · LIVE

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

2:14 min

Differences between traditional MLOps and GenAIOps

Maxim Salnikov Maxim Salnikov · WWC 2025

3:40 min

Navigating the MLOps tooling landscape and vendor decisions

Bas Geerdink · LIVE

2:15 min

Bridging the gap between model management and devops

Joy Joy · WWC 2024

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