What DevOps can learn from Oktoberfest

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An Oktoberfest Analogy for Application Performance & DevOps: What we can learn from Oktoberfest and how that applies to building high quality software

Software

As Delivered at Dynatrace PERFORM 2014 Opening Keynote
APM Culture: What we can learn from Oktoberfest
1 #Perform2014

“A Great Culture for Everyone… ”
2 #Perform2014

“Still staying civilized”
9 #Perform2014

Some metrics from 2013
6.3M Visitors 6.7M Mass Beer
1 Lost Segway 350 Lost iPhones
800 lost wallets 8 lost kids
10 #Perform2014

Oktoberfest <->
Dynatrace?
11 #Perform2014

Teamwork All Environments
APM for Everyone
Organized and Efficient Share Fun & Pain
13 #Perform2014

Our Goal: APM for Everyone
14 #Perform2014

Empowered by: Worlds-Best Community
15 #Perform2014

Encourage: Sharing Successes
16 #Perform2014

Free Trial Success Story
Robert working on Tekaf.com
Low Hanging Fruit on Dev Machine
• 75% Roundtrip Reduction
• 20% Faster Server Time
Real life problems in Production using dynaTrace APMaaS
• 4.5s delay caused by sporadic DNS lookup problems
• 2min delay caused by bad URLs
• 50% CPU consumption of custom RegEx
17 #Perform2014

Performance Spikes on
Product Page
18 #Perform2014

Re-occurring
Never seen in DEV
19 #Perform2014

DNS Lookup Issue
during Login
21 #Perform2014

Reverse Lookup by
IP fails on these IPs
22 #Perform2014

We need you …
… to reach the critical MASS 
24 #Perform2014

http://bit.ly/dttrial
http://bit.ly/onlineperfclinic
25 #Perform2014

@grabnerandi
@kenzenhofer
@gottesheim
@metmajer
@hzeitlhofer
26 #Perform2014

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HR Software Buyers Guide in 2024 - HRSoftware.com

Fatema Valibhai

10 Trends Likely to Shape Enterprise Technology in 2024

Mind IT Systems

Foundation models are machine learning models which are easily capable of performing variable tasks on large and huge datasets. FMs have managed to get a lot of attention due to this feature of handling large datasets. It can do text generation, video editing to protein folding and robotics. In case we believe that FMs can help the hospitals and patients in any way, we need to perform some important evaluations, tests to test these assumptions. In this review, we take a walk through Fms and their evaluation regimes assumed clinical value. To clarify on this topic, we reviewed no less than 80 clinical FMs built from the EMR data. We added all the models trained on structured and unstructured data. We are referring to this combination of structured and unstructured EMR data or clinical data.

Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...

harshavardhanraghave

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