Hauke Brammer

DevOps for Machine Learning

Your model works in training, but what about production? Learn how MLOps prevents model degradation and turns experiments into reliable, continuously monitored systems.

DevOps for Machine Learning
#1about 4 minutes

Defining MLOps and its role in production ML

MLOps is introduced as the DevOps equivalent for machine learning, addressing common failure points like duplicated effort, lack of reproducibility, and poor monitoring.

#2about 4 minutes

How ML development differs from software engineering

Machine learning development is an iterative, experimental process where most attempts fail, unlike the more linear and predictable workflow of traditional software engineering.

#3about 8 minutes

Building versioned data pipelines with feature stores

A centralized feature store and versioned data pipelines prevent duplicated work and ensure consistency between training and production environments.

#4about 6 minutes

Using experiment tracking and model registries for reproducibility

An experiment tracker captures metadata, parameters, and results, while a model registry versions the trained models to ensure full reproducibility.

#5about 4 minutes

Exploring central server and edge deployment options

Models can be deployed on a central server via an API for scalability or on edge devices to improve privacy and reduce latency.

#6about 6 minutes

Monitoring model performance and handling concept drift

Continuously monitoring model inputs, outputs, and confidence scores is crucial for detecting performance degradation due to concept drift and enabling retraining.

#7about 5 minutes

Adopting MLOps with an evolutionary, team-based approach

Successful MLOps implementation requires an incremental adoption strategy and cross-functional teams where data scientists and software engineers collaborate closely.

#8about 6 minutes

Q&A on parallel computing, data versioning, and security

The Q&A session addresses using tools like Argo for parallel processing, strategies for versioning large datasets, and handling malicious user feedback in active learning systems.

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