Operationalizing AI pipelines is notoriously complex. For deep learning applications, the challenge is even greater, due to the complexities of the types of data involved. Without a holistic view of the pipeline, operationalization can take months, and will require many data science and engineering resources. In this blog post, I'll show you how to move deep learning pipelines from the research environment to production, with minimal effort and without a single line of code.

Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists. This unprecedented level of big data workloads hasn’t come without its fair share of challenges.

Organizations trust Snowflake with their sensitive data, such as their customers’ personal information. Ensuring that this information is governed properly is critical. First, organizations must know what data they have, where it is, and who has access to it. Data classification helps organizations solve this challenge.

Today’s applications run on data. Customers value applications not only for the functionality they provide, but also for the data itself. It may sound obvious, but without data, apps would provide little to no value for customers. And the data contained in these applications can often provide value beyond what the app itself delivers. This begs the question: Could your customers be getting more value out of your application data?

BigQuery is a serverless, highly scalable, and cost-effective data warehouse that customers love. Similarly, Dataflow is a serverless, horizontally and vertically scaling platform for large scale data processing. Many users use both these products in conjunction to get timely analytics from the immense volume of data a modern enterprise generates.