Data Platform DevOps Software Engineer 
ODSC - Open Data Science
OCTOBER 9, 2024
The rapid evolution of AI is transforming nearly every industry/domain, and software engineering is no exception. But how so with software engineering you may ask? These technologies are helping engineers accelerate development, improve software quality, and streamline processes, just to name a few.
AWS Machine Learning Blog
MARCH 5, 2025
He has touched on most aspects of these projects, from infrastructure and DevOps to software development and AI/ML. After earning his bachelors degree in software engineering and a masters in computer vision and machine learning from Polytechnique Montreal, Philippe joined AWS to put his expertise to work for customers.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
ODSC - Open Data Science
JULY 19, 2023
Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. Data Engineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure data platforms in this diagram are neither exhaustive nor prescriptive.
The MLOps Blog
AUGUST 3, 2023
This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. As you’ve been running the ML data platform team, how do you do that?
The MLOps Blog
OCTOBER 3, 2023
I started from tech, my first job was an internship at Google as a software engineer. I’m from Poland, and I remember when I got an offer from Google to join as a regular software engineer. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps.
AWS Machine Learning Blog
APRIL 8, 2025
Integrating model deployment into the service development process was a key initiative to enable data scientists and ML engineers to deploy and maintain those models. The ML platform empowers the building and evolution of ML systems. The iFoods ML platform, ML Go! It starts with the ML Go!
The MLOps Blog
MARCH 21, 2023
Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases. It would make sure that all development and deployment workflows use good software engineering practices. My Story DevOps Engineers Who they are?
Expert insights. Personalized for you.
40 
Let's personalize your content