How to deploy machine learning on Google Cloud Platform

Posted in: Advanced Analytics, Big Data, DevOps, Google Cloud Platform, Technical Track
What exactly is an ML task? Before building an ML model, you need first to specify what are you planning to accomplish with the data. Having this in mind helps you to identify what exactly the ML tasks are for your use case. Some broad ML tasks could be: data ingestion, feature transformation, supervised learning, semi-supervised learning, unsupervised learning, dimensionality reduction, active learning, reinforcement learning, and model prediction. This is too much material to cover in one blog post, so expect another blog post to cover all those tasks. In the meantime, you can find a general overview on Wikipedia.
In this case, we need a scheduling system to run our ML task. Cloud Composer uses Celery for that and Scheduler uses CronJobs.
In this situation, you have plenty of options to implement the ML task. Choosing between the available options will depend on other requirements such as security, performance and costs.
8. Orchestrated tasks manually triggered that takes any amount of time: Cloud Build - through Triggers, Cloud Composer(Airflow), Kubeflow.
Here, you are implementing an orchestrated workflow that will perform tasks in sequence depending on conditions. Each task may take a different amount of time to finalize. A modern ML workflow / pipeline usually involves running containers in a third container orchestrator environment. On GCP it can be Kubernetes or AI Platform.
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