Deploy & Continunous Integration - TDC Floripa 2015

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Júnior Rocha

we talked about deploy and continuous integration in TDC Floripa. Check it out.

Software Tecnologia

take a seat and let’s talk about this!
Deploy & Continuous Integration:
walking between ﬂowers and stones

WHO?
Junior Rocha 
Cloud Engineer
@juniorocha90
contato@juniorocha.com
/jrrocha90

Disclaimer!
* this is not about how use DevOps tools.
* neither the better way to do something.
SO… WTF I’M DOING HERE?
* far less to damn some tool.

Summary
* Continuous Integration
* Continuous Deployment
* Gênesis
* Continuous Delivery
* Questions?

Continuous Integration
Means:
- maintain a code repository.
- a lot of builds, every day.
- automate the tests.
- solve problems quickly.
- reduce production problems.
- devops culture?

CI Sample
source code
commit
ci process
build
test
report
repository
ci server

Continuous Deployment
"is the practice of releasing every good
build to users – a more accurate name
might have been 'continuous release'" .

Continuous Deployment
- put some code in production.

Continuous Deployment
- lead time reduced.

Continuous Deployment
source code
commit
ci process
build
test
report
repository
ci server
bug ﬁx
feature

Deployment vs Delivery?
Continuous
Deployment
Continuous Delivery

Continuous Deployment
- there’s many approaches to select
the right tool.

Continuous Deployment
- infrastructure as a code.

Continuous Deployment
- vagrant or docker?

Gênesis
500AC We use magnetic disk!
1971 We use FTP!

Gênesis
2000
2002
Subversion
Tortoise SVN1990 CVS
2005 GIT

Continuous Delivery
"set of processes, tools and techniques
for the rapid, reliable and continuous
development and delivery of software.

Continuous Delivery
- automate the code delivery.

Continuous Delivery
- do the right thing, and go the
right way.

Continuous Delivery
- the code release is a business
decision.

Delivery Pipeline
delivery team version control build & unit tests AAT UAT release
commit trigger
feedback

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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.

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

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Deploy & Continunous Integration - TDC Floripa 2015

1. take a seat and let’s talk about this! Deploy & Continuous Integration: walking between ﬂowers and stones

2. WHO? Junior Rocha  Cloud Engineer @juniorocha90 contato@juniorocha.com /jrrocha90

3. Disclaimer! * this is not about how use DevOps tools. * neither the better way to do something. SO… WTF I’M DOING HERE? * far less to damn some tool.

4. Summary * Continuous Integration * Continuous Deployment * Gênesis * Continuous Delivery * Questions?

5. Continuous Integration Means: - maintain a code repository. - a lot of builds, every day. - automate the tests. - solve problems quickly. - reduce production problems. - devops culture?

6. Continuous Integration

7. CI Sample source code commit ci process build test report repository ci server

8. Jenkins Job Example

9. Jenkins AWS Plugin

10. Jenkins Nodes

11. Continuous Deployment "is the practice of releasing every good build to users – a more accurate name might have been 'continuous release'" .

12. Continuous Deployment - put some code in production.

13. Continuous Deployment - lead time reduced.

14. Continuous Deployment - UAT for what?

15. Continuous Deployment source code commit ci process build test report repository ci server bug ﬁx feature

16. Deployment vs Delivery? Continuous Deployment Continuous Delivery

17. Continuous Deployment - there’s many approaches to select the right tool.