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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.
With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and MLengineers require capable tooling and sufficient compute for their work. JuMa is now available to all data scientists, MLengineers, and data analysts at BMW Group.
Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central dataplatform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.
The architecture maps the different capabilities of the MLplatform 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.
Machine Learning Operations (MLOps) can significantly accelerate how data scientists and MLengineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.
I switched from analytics to data science, then to machine learning, then to dataengineering, then to MLOps. For me, it was a little bit of a longer journey because I kind of had dataengineering and cloud engineering and DevOpsengineering in between. It’s two things. How awful are they?”
Stefan is a software engineer, data scientist, and has been doing work as an MLengineer. He also ran the dataplatform in his previous company and is also co-creator of open-source framework, Hamilton. As you’ve been running the MLdataplatform team, how do you do that?
From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and MLengineers to build and deploy models at scale.
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