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Author SHA1 Message Date
Ben Shaw 6788cd43b9
Merge 441d41a44a into 5c5ed195f5 2024-02-16 01:18:10 +00:00
ben 441d41a44a refined scribs platform section 2024-02-15 17:18:12 -08:00
R Tyler Croy 5c5ed195f5
Merge pull request #135 from scribd/dependabot/bundler/nokogiri-1.16.2 
Bump nokogiri from 1.14.3 to 1.16.2
 2024-02-07 09:46:35 -08:00
dependabot[bot] 3fb64427ff
Bump nokogiri from 1.14.3 to 1.16.2 
Bumps [nokogiri](https://github.com/sparklemotion/nokogiri) from 1.14.3 to 1.16.2.
- [Release notes](https://github.com/sparklemotion/nokogiri/releases)
- [Changelog](https://github.com/sparklemotion/nokogiri/blob/main/CHANGELOG.md)
- [Commits](https://github.com/sparklemotion/nokogiri/compare/v1.14.3...v1.16.2)

---
updated-dependencies:
- dependency-name: nokogiri
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
 2024-02-06 03:31:41 +00:00
2 changed files with 6 additions and 10 deletions
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Gemfile.lock
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@ -231,7 +231,7 @@ GEM
jekyll-seo-tag (~> 2.1)
minitest (5.17.0)
multipart-post (2.1.1)
nokogiri (1.14.3-x86_64-linux)
nokogiri (1.16.2-x86_64-linux)
racc (~> 1.4)
octokit (4.22.0)
faraday (>= 0.9)
@ -239,7 +239,7 @@ GEM
pathutil (0.16.2)
forwardable-extended (~> 2.6)
public_suffix (4.0.7)
racc (1.6.2)
racc (1.7.3)
rb-fsevent (0.11.1)
rb-inotify (0.10.1)
ffi (~> 1.0)

12
_posts/2024-02-05-evolution-of-mlplatform.md
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@ -86,31 +86,27 @@ MLOps is a methodology that provides a collection of concepts and workflows desi
Scribd's ML Platform -- MLOps and Platforms in Action
-------------------------------------
At Scribd we have developed a machine learning platform which provides a curated developer experience for machine learning developers and applies the concepts of DevOps in the following ways
At Scribd we have developed a machine learning platform which provides a curated developer experience for machine learning developers. This platform has been built with MLOps in mind which can be seen through its use of common DevOps principles.
1. **Automation:**
1. **Automation:**
* Applying CI/CD strategies to model deployments through the use of Jenkins pipelines which deploy models from the Model Registry to AWS based endpoints.
* Automating Model training throug the use of Airflow DAGS and allowing these DAGS to trigger the deployment pipelines to deploy a model once re-training has occured.
2. **Continuous** **Testing:**
* Applying continuous testing as part of a model deployment pipeline, removing the need for manual testing.
* Increased tooling to support model validation testing.
3. **Monitoring:**
* Monitoring real time inference endpoints
* Monitoring training DAGS
* Monitoring batch jobs
4. **Collaboration and Communication:**
* Feature Store which provides feature discovery and re-use
* Model Database which provides model collaboration
6. **Version Control:**
* Applyied version control to experiments, machine learning models and features
* Applying version control to experiments, machine learning models and features
References