Data Platform Data Scientist DevOps 
AWS Machine Learning Blog
FEBRUARY 21, 2025
Steep learning curve for data scientists: Many of Rockets data scientists did not have experience with Spark, which had a more nuanced programming model compared to other popular ML solutions like scikit-learn. This created a challenge for data scientists to become productive.
AWS Machine Learning Blog
APRIL 8, 2025
Solution overview The following diagram illustrates iFoods legacy architecture, which had separate workflows for data science and engineering teams, creating challenges in efficiently deploying accurate, real-time machine learning models into production systems. The ML platform empowers the building and evolution of ML systems.
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NOVEMBER 24, 2023
In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.
AWS Machine Learning Blog
FEBRUARY 27, 2024
Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.
IBM Journey to AI blog
SEPTEMBER 20, 2023
These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps. IBM watsonx consists of the following: IBM watsonx.ai
AWS Machine Learning Blog
OCTOBER 20, 2023
The architecture maps the different capabilities of the ML platform to AWS accounts. The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake.
AWS Machine Learning Blog
OCTOBER 3, 2024
This approach led to data scientists spending more than 50% of their time on operational tasks, leaving little room for innovation, and posed challenges in monitoring model performance in production. This feature integrates with Amazon SageMaker Experiments to provide data scientists with insights into the tuning process.
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