Visualization of three datastreams contributing to a single stream Visualization of three datastreams contributing to a single stream

Devel­op­ment and Imple­ment­a­tion of a Fully Auto­mated MLOps Platform

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Visualization of three datastreams contributing to a single stream
Suc­cess stories

Com­mu­nic­a­tions, Media & Telecom  - Devel­op­ment and Imple­ment­a­tion of a Fully Auto­mated MLOps Platform

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Com­mu­nic­a­tions, Media & Telecom

The imple­ment­a­tion of a fully auto­mated MLOps plat­form sig­ni­fic­antly reduced the time and effort for the data sci­ence teams and enabled three times faster deploy­ment of new and exist­ing ML models.

Ini­tial situation

The cus­tom­er’s machine learn­ing mod­els had to be manu­ally trained, tuned for hyper­para­met­ers, hos­ted, and deployed, and the track­ing of exper­i­ments was done manu­ally. This caused sig­ni­fic­ant time and effort for the data sci­ence teams with each model change or new ML use case.

Archi­tec­ture

The archi­tec­ture is built on AWS, using tech­no­lo­gies such as Apache Air­flow, Apache Spark, Amazon Glue, Amazon Aurora & Amazon­Sage­Maker, Ter­ra­form, Git­Hub, FastAPI, Splunk Observ­ab­il­ity, and Mlflow.

Gen­er­ated bene­fits

As a res­ult, there are no costs for “mov­ing to pro­duc­tion” since all work­flows of the data sci­ent­ists run in the pro­duc­tion envir­on­ment and use a spe­cial IAM author­iz­a­tion model. New ML mod­els are developed in an autonom­ous work­flow by the data sci­ent­ists and deployed live in pro­duc­tion three times faster than before, includ­ing auto­mated model train­ing, exper­i­ment track­ing, model host­ing, and serving.

Ser­vices accom­plished by synvert 

The synvert team has imple­men­ted an ML plat­form that is fully auto­mated, test­able, observ­able, and well-doc­u­mented. Synvert developed a mod­u­lar prin­ciple sup­por­ted by declar­at­ive REST­ful ser­vices and man­aged com­pon­ents, which together min­im­ize the need for DevOps or engin­eer­ing sup­port and ensure rapid deploy­ment of new and exist­ing models.

Both exist­ing and new ML use cases oper­ate in a highly avail­able and stable envir­on­ment, thanks to sig­ni­fic­ant invest­ments in test­ing strategies.

Categories
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Banking
Communications, Media & Telecom
Consumer Goods & Retail
Energy & Resources
Health & Life Science
Industrials
Insurance
Public Sector
Com­mu­nic­a­tions, Media & Telecom

Con­sol­id­at­ing Big Data & BI at a Major Media Powerhouse
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Vehicle Data Store
Indus­tri­als

Soft­ware­Fact­ory at a Lead­ing Auto­mot­ive Soft­ware Company
Con­sumer Goods & Retail

DevX Impact at a Fin­ance Company
Con­sumer Goods & Retail

Driv­ing Inter­na­tional Expan­sion for a Lead­ing Online Car Seller
Indus­tri­als

Accel­er­at­ing Cloud-Nat­ive Devel­op­ment for a Car Manufacturer
Com­mu­nic­a­tions, Media & Telecom

Retail Com­mis­sion Dia­gnostics Anom­aly Detection
Energy & Resources

Set­ting up a Reli­able and Power­ful Platform
Com­mu­nic­a­tions, Media & Telecom

Pre­dic­tion of cus­tomer prob­ab­il­ity to churn
Indus­tri­als

Early Detec­tion of Mech­an­ical Issues
Indus­tri­als

NLP​ in Auto­mot­ive Reporting
Com­mu­nic­a­tions, Media & Telecom

Oper­a­tional Data Lake
Con­sumer Goods & Retail

Real-Time Mar­ket­ing
Indus­tri­als

Auto­matic clas­si­fic­a­tion of cars by their model
Pub­lic Sector

Data plat­form and cata­log for com­pens­a­tion claims
Com­mu­nic­a­tions, Media & Telecom

Data Recon­cili­ation
Indus­tri­als

Cent­ral­ized Data Warehouse
Indus­tri­als

Data as a Ser­vice – BI & Big Data CC
Com­mu­nic­a­tions, Media & Telecom

Oper­a­tional Work/Support in Data Warehousing
Indus­tri­als

Erosion Detec­tion with Com­puter Vision
Com­mu­nic­a­tions, Media & Telecom

Devel­op­ment and Imple­ment­a­tion of a Fully Auto­mated MLOps Platform
Con­sumer Goods & Retail

Pre­dic­tion and Alloc­a­tion Optim­iz­a­tion for sta­tion­ary Channels
Com­mu­nic­a­tions, Media & Telecom

Build­ing a Near Real-Time Recom­mend­a­tion System
Con­sumer Goods & Retail

Migra­tion AWS data plat­form to Google Cloud
Com­mu­nic­a­tions, Media & Telecom

Design and Devel­op­ment of a Google Cloud-Based Data Platform
Con­sumer Goods & Retail

Refact­or­ing, shift and new devel­op­ment of Data Ser­vices in Google Cloud

Estab­lish­ment of a Mas­ter Data Man­age­ment Foundation
Indus­tri­als

Cent­ral Data Lake

Replace­ment of a com­plex IBM Data­stage structure

CRM ana­lyt­ics and lead management

Stand­ard­iz­a­tion of eval­u­ation processes

Devel­op­ment of an ERP data hub

Estab­lish­ment and fur­ther devel­op­ment of a DWH

Devel­op­ment of a group-wide BI platform

Estab­lish­ment of a new sales report­ing system

Source data migra­tion with auto­ma­tion of the source data connection

Cloud self-ser­vice BI for data-driven cor­por­ate management

Devel­op­ment of vari­ous ML use cases with AWS

Replace­ment of an in-house soft­ware development

Com­pany-wide data integration

Imple­ment­a­tion of real-time data links
Con­sumer Goods & Retail

Smart Data Catalog

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