WeAreDevelopers Logo WeAreDevelopers
Sign in
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.

Pause
Mute Enter Fullscreen
#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 · World Congress 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 · World Congress 2024

1:07 min

Navigating the complexities of machine learning model lifecycles

Iryna Kondrashchenko Iryna Kondrashchenko +1 · World Congress 2025

More from World Congress 2021

Micro Frontends with Module Federation: Why and How? thumbnail
45:40
about 5 years ago

Micro Frontends with Module Federation: Why and How?

Manfred Steyer

Data Science on Software Data thumbnail
48:10
about 5 years ago

Data Science on Software Data

Markus Harrer

The journey from developer to devops - what i've learnt along the way thumbnail
42:44
about 5 years ago

The journey from developer to devops - what i've learnt along the way

Liam Hurrel, Alireza Chegini

How Data is Shaping our Games thumbnail
43:30
about 5 years ago

How Data is Shaping our Games

Johanna Pirker

Cross platform Augmented Reality development with React Native thumbnail
40:50
about 5 years ago

Cross platform Augmented Reality development with React Native

Vladimir Novick

How to Build a Monetization Strategy Based on User's in-app Behavior thumbnail
38:59
about 5 years ago

How to Build a Monetization Strategy Based on User's in-app Behavior

Natalie Sieradzki

Related videos

Is my AI alive but brain-dead? How monitoring can tell you if your machine learning stack is still performing thumbnail
48:07
LIVE • almost 5 years ago

Is my AI alive but brain-dead? How monitoring can tell you if your machine learning stack is still performing

Lina Weichbrodt

The state of MLOps - machine learning in production at enterprise scale thumbnail
47:19
LIVE • over 4 years ago

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

Bas Geerdink

DevOps for Machine Learning thumbnail
44:40
World Congress 2021 • about 5 years ago

DevOps for Machine Learning

Hauke Brammer

Effective Machine Learning - Managing Complexity with MLOps thumbnail
45:45
World Congress 2021 • about 5 years ago

Effective Machine Learning - Managing Complexity with MLOps

Simon Stiebellehner

MLOps - What’s the deal behind it? thumbnail
29:24
World Congress 2022 • over 4 years ago

MLOps - What’s the deal behind it?

Nico Axtmann

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment thumbnail
25:41
World Congress 2024 • about 2 years ago

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment

Joy

Related articles

From learning to earning

Jobs that call for the skills explored in this talk.