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WeAreDevelopers LIVE • Mar 25, 2022

The state of MLOps - machine learning in production at enterprise scale

Bas Geerdink

Successful MLOps depends less on vendor tools and more on rigid software engineering. Learn how to transition from isolated notebooks to robust, cross-functional ML pipelines at enterprise scale.

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#1 about 2 min

Introduction to the speaker and data science background

The evolution from artificial intelligence research into modern data engineering establishes a foundational operational context.

#2 about 1 min

Initial challenges of machine learning in production

Moving beyond isolated algorithms involves establishing robust ecosystems for live operational modeling.

#3 about 2 min

Limitations of notebook development for production environments

While jupyter notebooks excel in experimentation, they lack the stability required for scalable production application.

#4 about 2 min

Integrating machine learning into broader enterprise software systems

Core machine learning code forms only a small fraction of the complex infrastructure needed for production maintenance.

#5 about 5 min

Adopting a DevOps culture for machine learning pipelines

Embracing cross-functional collaboration eliminates operational handoffs and increases trust when deploying continuous machine learning updates.

#6 about 3 min

Defining core roles and responsibilities in MLOps teams

Designing cohesive delivery groups requires integrating the distinct skills of data scientists and operational engineers.

#7 about 4 min

Best practices for maintaining production machine learning models

Treating models as versioned artifacts enables continuous monitoring and retraining against ongoing dataset drift.

#8 about 6 min

Architectural patterns for batch and real-time pipelines

Designing sustainable systems separates heavy batch training jobs from lightweight real-time prediction serving endpoints.

#9 about 1 min

Real-world application of MLOps architectural patterns at Wayfair

Combining cloud-native vendor solutions with custom internal tools successfully powers large-scale retail machine learning pipelines.

#10 about 4 min

Navigating the MLOps tooling landscape and vendor decisions

Engineering teams must carefully evaluate the tradeoffs between building custom infrastructure and adopting commercial automated solutions.

#11 about 5 min

Decoupling model serving from core application logic

Isolating models as dedicated microservices allows for independent updates without risking the entire software application.

#12 about 3 min

Handling near real-time model evaluation in streaming contexts

Embedding predictive models directly into stream processing engines strictly satisfies necessary sub-second latency performance requirements.

#13 about 2 min

Standardizing deployments with containers and Kubernetes orchestration

Packing algorithms into structured docker containers ensures reliable execution across varied cloud instances and local environments.

#14 about 3 min

Centralizing data preparation with a dedicated feature store

Deploying specialized databases unifies offline training data retrieval with high-performance real-time prediction querying.

#15 about 3 min

Managing batch job complexities through workflow orchestration

Implementing sophisticated scheduling frameworks helps teams effectively track, debug, and automate model retraining tasks.

#16 about 2 min

Summary and recommended educational resources for MLOps

Exploring dedicated organization literature and technical vendor certifications empowers practitioners to deepen architectural expertise.

#17 about 7 min

Audience Q&A on educational courses and freelancing prospects

Practical audience inquiries highlight valuable learning materials, field entry strategies, and independent consulting career navigation.

Matching moments

5:28 min

Defining MLOps and its role in production systems

Hauke Brammer · World Congress 2023

1:26 min

Transitioning machine learning models from notebooks to production

Kilian Kluge +1 · World Congress 2022

4:19 min

Introduction to DevOps for AI and MLOps

Aarno Aukia · LIVE

2:14 min

Differences between traditional MLOps and GenAIOps

Maxim Salnikov Maxim Salnikov · World Congress 2025

2:51 min

Moving machine learning models from experimentation to production

Simon Stiebellehner · World Congress 2021

2:15 min

Bridging the gap between model management and devops

Joy Joy · World Congress 2024

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