Machine Learning is all the rage now. However, you can't do Machine Learning without data. A lot of data is stored in databases, and BigQuery is Google's analytics offering that lets you store and analyze data easily. In the past, there has always been a gap between data analysis and ML engineering, simply because analysts mostly use SQL while ML engineers use python. How can you bridge the gap?
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Machine Learning is all the rage now. However, you can't do Machine Learning without data. A lot of data is stored in databases, and BigQuery is Google's analytics offering that lets you store and analyze data easily. In the past, there has always been a gap between data analysis and ML engineering, simply because analysts mostly use SQL while ML engineers use python. How can you bridge the gap?
Join us to learn about Machine Learning using SQL.
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