Building Production Machine Learning Systems on Google Cloud Platform (Part 2)

Building Production Machine Learning Systems on Google Cloud Platform (Part 2)

In this article, we will continue on building production machine learning systems on GCP with a special focus on the following: Data ingestion for cloud-based analytics and ML (part 2); Adaptable ML system (part 2)

In this article, we will continue on building production machine learning systems on GCP with a special focus on the following:

  • Data ingestion for cloud-based analytics and ML (part 2)
  • Adaptable ML system (part 2)

This is a descriptive series at a high-level, there will be another series on implementing some of these standard concepts but if you would love to get fully hands-on before then, I suggest you take the [Advance Machine Learning with Tensorflow on GCP_](https://www.coursera.org/programs/697b2a08-db87-4463-a112-e1ac8c46b181?collectionId=&productId=GWIdT4bQEeiFWQrjbTVkyg&productType=s12n&showMiniModal=true) course by google ML-Team for a start._

Data Ingestion for Cloud-based analytics and ML

In the first part of this series, we looked at the different components that make up a production machine learning system and the various options available on GCP. We have established that all ML system starts with the data ingestion unit which handles how data is being fed into the system.

The first step to leveraging the cloud for data analytics and ML is to have the data in the cloud environment. This could be done with little effort if the amount of data to be migrated is small but the reverse is the case for large amounts of data. Some data migration challenges are large amounts of data, too much bandwidth, checksumming, encryption, firewalls, time, and resources. With GCP, data migration is done with some level of flexibility and easiness. The best choice of migration method depends on where your data is and your budget.

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