125 captures
21 Mar 2016 - 21 Sep 2026
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About this capture
TIMESTAMPS
The Wayback Machine - http://web.archive.org/web/20240804145807/https://devops.com/category/blogs/roelbob/
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Hot Topics
The Software Extinction Event That Wasn’t
BMC Brings Generative AI to the Mainframe
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Delta CEO Puts Price Tag on CrowdStrike Damage: $500 Million
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ROELBOB
Dating Material
ROELBOB
|
July 31, 2024
|
humor
,
parody
,
satire
Image Recognition
ROELBOB
|
July 23, 2024
|
humor
,
image recognition
,
parody
,
satire
The Issues at Hand
ROELBOB
|
July 15, 2024
|
cartoon
,
satire
Choice
ROELBOB
|
July 8, 2024
|
humor
,
parody
,
satire
A Matter of Attitude
ROELBOB
|
July 1, 2024
|
humor
,
parody
,
perceptions of reality
,
satire
Logging Out
ROELBOB
|
June 26, 2024
|
digital immortality
,
humor
,
parody
,
satire
The Devil and the Details
ROELBOB
|
June 18, 2024
|
dashboards
,
GUI
,
homor
,
humor
,
parody
,
satire
AI Training
ROELBOB
|
June 10, 2024
|
ai
,
AI training
,
humor
,
satire
,
training
The Prerogative of Power
ROELBOB
|
June 5, 2024
|
corporate governance
,
humor
,
parody
,
satire
Open to Work
ROELBOB
|
May 29, 2024
|
devops jobs
,
humor
,
parody
,
satire
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AI/ML Feature Store
Step
1
of
6
16%
What are the primary challenges you face in data preparation for ML pipelines?
(Required)
Feature creation
Model accuracy
Model performance
Data preparation and integration - Extract, Transform, and Load (ETL) processes
Ensuring data quality and consistency
Slow, inefficient or inconsistent communication between workflow participants
Scaling up AI/ML feature set pipelines
Rank the following phases based on the level of difficulty in your ML pipeline deployment process (Rank from most to least important)
Creating feature sets
Experimenting with models
Model tuning
Deploying models to production
AI agent feature store access
Which of the following solutions are you currently exploring to address data prep and pipeline deployment challenges?
(Required)
Automated data preprocessing tools
Advanced feature engineering frameworks
End-to-end ML pipeline platforms
Copilots for feature set workflows
AI agents
Enhanced collaboration and communication tools
How significant are communication and handoff issues between domain experts, data scientists, and ML engineers in delaying your ML projects?
(Required)
Extremely significant
Very significant
Moderately significant
Slightly significant
Not significant
On average, how long does it take for your team to move from raw data to a production-ready ML model?
(Required)
Less than 1 month
1-2 months
2-4 months
More than 4 months
What impact do delays in deploying AI/ML models have on your business operations?
(Required)
Significant lost business opportunities
Moderate impact on business processes
Minimal impact
No noticeable impact
Δ
