Simon Stiebellehner
Effective Machine Learning - Managing Complexity with MLOps
#1about 8 minutes
Understanding why most machine learning projects fail to deliver value
Many ML projects fail despite mature tools and skilled engineers because organizations underestimate the complexity of the full production lifecycle.
#2about 4 minutes
The consequences of unmanaged ML complexity
Ignoring the full ML lifecycle leads to a deployment gap, inefficient manual work, and slow iteration speeds that prevent models from delivering value.
#3about 10 minutes
Analyzing a typical manual machine learning workflow
A case study reveals common pain points in a manual process, including poor reproducibility, inconsistency, and a slow handover to DevOps.
#4about 11 minutes
Designing an ideal automated MLOps process
A best-practice MLOps workflow automates the entire lifecycle using components like a feature store, orchestrated pipelines, and a model registry.
#5about 9 minutes
Choosing between a custom vs managed MLOps platform
Evaluate the trade-offs between building a custom platform with open-source tools versus adopting a managed cloud platform like AWS SageMaker.
#6about 3 minutes
Creating a stepwise transition strategy to MLOps
Adopt MLOps incrementally by first tackling the biggest pain points, such as the deployment gap, to deliver value quickly.
Related jobs
Jobs that call for the skills explored in this talk.
Picnic Technologies B.V.
Amsterdam, Netherlands
Intermediate
Senior
Python
Structured Query Language (SQL)
+1
WALTER GROUP
Wiener Neudorf, Austria
Intermediate
Senior
Python
Data Vizualization
+1
ROSEN Technology and Research Center GmbH
Osnabrück, Germany
Senior
TypeScript
React
+3
Matching moments
01:32 MIN
Organizing a developer conference for 15,000 attendees
Cat Herding with Lions and Tigers - Christian Heilmann
04:57 MIN
Increasing the value of talk recordings post-event
Cat Herding with Lions and Tigers - Christian Heilmann
03:28 MIN
Why corporate AI adoption lags behind the hype
What 2025 Taught Us: A Year-End Special with Hung Lee
03:39 MIN
Breaking down silos between HR, tech, and business
What 2025 Taught Us: A Year-End Special with Hung Lee
04:27 MIN
Moving beyond headcount to solve business problems
What 2025 Taught Us: A Year-End Special with Hung Lee
03:48 MIN
Automating formal processes risks losing informal human value
What 2025 Taught Us: A Year-End Special with Hung Lee
03:38 MIN
Balancing the trade-off between efficiency and resilience
What 2025 Taught Us: A Year-End Special with Hung Lee
04:22 MIN
Why HR struggles with technology implementation and adoption
What 2025 Taught Us: A Year-End Special with Hung Lee
Featured Partners
Related Videos
DevOps for Machine Learning
Hauke Brammer
MLOps - What’s the deal behind it?
Nico Axtmann
The state of MLOps - machine learning in production at enterprise scale
Bas Geerdink
Deployed ML models need your feedback too
David Mosen
The State of GenAI & Machine Learning in 2025
Alejandro Saucedo
From Traction to Production: Maturing your LLMOps step by step
Maxim Salnikov
DevOps for AI: running LLMs in production with Kubernetes and KubeFlow
Aarno Aukia
The Road to MLOps: How Verivox Transitioned to AWS
Elisabeth Günther
Related Articles
View all articles.gif?w=240&auto=compress,format)
.gif?w=240&auto=compress,format)

.gif?w=240&auto=compress,format)
From learning to earning
Jobs that call for the skills explored in this talk.

ASFOTEC
Canton de Lille-6, France
Senior
GIT
Bash
DevOps
Python
Gitlab
+6

Spait Infotech Private Limited
Sheffield, United Kingdom
Remote
£55-120K
Intermediate
ETL
Azure
Scrum
+12

Da Vinci Engineering GmbH
Reutlingen, Germany
Intermediate
Azure
DevOps
Python
Docker
PyTorch
+6

FitNext Co
Charing Cross, United Kingdom
Remote
Intermediate
DevOps
Python
Docker
Grafana
+6

Scalian Groupe
Paris, France
Remote
€55K
Senior
API
C++
Linux
+11



Talent Connect
Municipality of Madrid, Spain
Bash
Azure
DevOps
Python
Docker
+9
