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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.
Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.
Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.
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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