MLOps Community
timezone
+00:00 GMT
SIGN IN
  • Home
  • Events
  • Content
  • People
  • Messages
  • Channels
  • Help
Sign In
Sign in or Join the community to continue

MLOps in Practice: Common Challenges and Lessons Learned

Posted Nov 24, 2022 | Views 180
# Machine Learning Reply
# MLOps Integration
# Levels of Maturity
Share
SPEAKERS
Marouen Hizaoui
Marouen Hizaoui
Marouen Hizaoui
Senior Consultant @ Machine Learning Reply

Graduating in 2017 with a degree in Software engineering specializing in intelligent and decision support systems, Marouen has worked for different companies in different industries getting involved in projects of different maturities and different scopes with them being data products as the common denominator.

Marouen experienced the need for MLOps firsthand before the concept became mainstream, and he has even started applying the different principles in a few of the projects that he has been involved in from a need basis.

Marouen joined ML Reply in mid-2021 and has been working on supporting their different clients through their MLOPS journey as well as raising their company's collective knowledge on MLOps ever since.

+ Read More

Graduating in 2017 with a degree in Software engineering specializing in intelligent and decision support systems, Marouen has worked for different companies in different industries getting involved in projects of different maturities and different scopes with them being data products as the common denominator.

Marouen experienced the need for MLOps firsthand before the concept became mainstream, and he has even started applying the different principles in a few of the projects that he has been involved in from a need basis.

Marouen joined ML Reply in mid-2021 and has been working on supporting their different clients through their MLOPS journey as well as raising their company's collective knowledge on MLOps ever since.

+ Read More
Mo Basirati
Mo Basirati
Mo Basirati
Senior Consultant/MLOps Lead @ Machine Learning Reply

Mo is a senior consultant working for MLReply since 2021. Mo led system architecture and design, set up MLOps pipelines, and built and deployed ML apps (on cloud and on-prem) for top clients in Germany.

Mo also leads the team of the MLOps community of practice at MLReply, where they enable their internal and external capacities of MLOps practices. Backed by a strong background in software engineering, Mo also received a Ph.D. degree in information systems in 2020.

+ Read More

Mo is a senior consultant working for MLReply since 2021. Mo led system architecture and design, set up MLOps pipelines, and built and deployed ML apps (on cloud and on-prem) for top clients in Germany.

Mo also leads the team of the MLOps community of practice at MLReply, where they enable their internal and external capacities of MLOps practices. Backed by a strong background in software engineering, Mo also received a Ph.D. degree in information systems in 2020.

+ Read More
SUMMARY

The different practices and approaches that would ensure the success of your data products, distilled from doing MLOps at different clients from different industries and different levels of maturity:

  • It's much more than technical stuff
  • The "best" tool is not always the best solution
  • Integrating MLOps in system and infrastructure with different levels of maturity
+ Read More

Watch More

54:29
Posted May 08, 2021 | Views 266
40:00
Posted Apr 07, 2022 | Views 349
# ML Platform
# Forecasting and Optimization
# Flexibility