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For You LIVE
World Congress 2021 • Jun 28, 2021

Deployed ML models need your feedback too

David Mosen

Standard infrastructure tracking won't catch concept drift in your ML models. Learn how to build continuous feedback loops that directly link ground-truth performance to business OKRs.

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#1 about 2 min

Defining machine learning operations and continuous training pipelines

Extending continuous integration and delivery with continuous training practices keeps algorithms performing accurately in live production environments.

#2 about 3 min

Transitioning through maturity levels of machine learning operations

Reaching full deployment automation requires active system monitoring and intelligent feedback loops to trigger retraining cycles automatically.

#3 about 5 min

Designing architectures for automated machine learning lifecycles

Integrating feature stores and prediction services within code repositories highlights the necessity of robust monitoring for deployment stability.

#4 about 4 min

Addressing input distribution changes and concept drift

Shifting baselines in input variables or their initial relationship to target outputs mandate teams to isolate algorithmic drift and relabel dataset pipelines.

#5 about 5 min

Correlating technical performance with high-level business metrics

Directly monitoring proxy metrics and model degradation surfaces critical insights into overall system stability and key business performance indicators.

#6 about 3 min

Managing delays and automation in model feedback collection

Connecting automated feedback streams to decoupled evaluation routines ensures teams can detect and remediate concept drift before prediction staleness compounds.

#7 about 5 min

Overcoming limitations in current machine learning monitoring tools

Current managed cloud environments and open-source stacks frequently lack business-layer monitoring loops and require extensive engineering configurations to maintain.

#8 about 4 min

Integrating native APIs for custom model monitoring profiles

Abstracting assessment through flexible model profiles allows engineering teams to track complex regression metrics directly across arbitrary management platforms.

#9 about 5 min

Visualizing model metrics and business indicators in context

Combining real-time technical health statistics with delayed business trends like user churn establishes a holistic view of overall software accuracy.

#10 about 6 min

Standardizing machine learning deployments for varying schemas and environments

Shifting deployment models requires teams to leverage proxy metrics when adapting standardized codebase templates to edge computing hardware or unstructured environments.

Matching moments

4:01 min

Transitioning artificial intelligence into operational business environments

Stefan Donsa Stefan Donsa +1 · LIVE

5:28 min

Defining MLOps and its role in production systems

Hauke Brammer · WWC 2023

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt · LIVE

3:08 min

Maintaining and monitoring machine learning models in production

Natalie Pistunovich · LIVE

2:15 min

Bridging the gap between model management and devops

Joy Joy · WWC 2024

1:07 min

Navigating the complexities of machine learning model lifecycles

Iryna Kondrashchenko Iryna Kondrashchenko +1 · WWC 2025

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