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Study mode:
on
1
intro
2
preamble
3
intro
4
agenda
5
scaling is hard
6
problem
7
context
8
approach
9
load test one host
10
retrieve data from aws to pandas
11
analyse one host performance
12
we can do better
13
another thing - latency
14
approaches
15
create a scaling simulator
16
create a traffic shape generator
17
and run a first local experiment
18
what does it mean?
19
first attempt
20
find the best parameters
21
and the winner is
22
which actually looks better
23
testing new parameters in production
24
simulation versus real life
25
conclusion
26
code
27
about gustavo
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore a conference talk that delves into optimizing AWS DevOps practices using Python and machine learning techniques. Learn how to tackle scaling challenges in cloud infrastructure by leveraging data analysis and simulation. Discover methods for retrieving AWS data with pandas, analyzing host performance, and creating scaling simulators. Gain insights into traffic shape generation, local experimentation, and parameter optimization. Compare simulation results with real-life production testing, and understand the practical implications for improving cloud infrastructure efficiency. Acquire valuable knowledge on integrating Python and machine learning to enhance AWS DevOps workflows and decision-making processes.

Empowering AWS DevOps with Python and Machine Learning

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