Build unified batch and streaming pipelines on popular ML frameworks

According to IDC, by the end of 2024 75% of enterprises will shift from piloting to operationalizing artificial intelligence, yet the growing complexity of data types, heterogeneous data stacks, and programming languages make this a challenge for data engineers. Join this session to learn how the latest Google Cloud data innovations enable a mutli-national organization to create real-time, personalized offers to customers, by constructing high-performing data pipelines using the language they prefer. Additionally, they "plug in" to their preferred ML frameworks such as PyTorch, Tensorflow, and scikit-learn for models training and production workloads, while having Dataflow automatically execute this multi-language pipeline, at scale and in real time, with no need to concern themselves about falling behind on data due to capacity constraints or cost overruns.

Speaker: Sachin Agarwal

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