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"In this course, I learned How to use the Google cloud platform(GCP) and it's tools like BigQuery, Cloud Storage, Vision, Dataproc, Pub/Sub, Dataflow, compute engine, etc.\nIn GCP, we can generate the instance of Virtual Machine(VM). It's a serverless platform (Google has it's own data centers). We can develop a complete software through GCP.\nIN GCP, we can build custom models. It is very handy to operate for BigData. The Data in GB, TB, or PB can be processed in seconds or minutes on GCP.\n<text />Also, I deployed the ML model for Classifying Images with Pre-built ML Models using Cloud Vision API and AutoML.\nIn this model, we classified the Images of clouds in three categories., viz cirrus, cumulonimbus, and cumulus.\n<text />The cool features of AutoML and Vision API-\n-We don't have to write code for building the machine learning(ML) model.\n-AutoML decides the dataset splits for training and testing.\n-If you are working with a dataset that isn't already labeled, AutoML Vision provides an in-house human labeling service.\n- We have to just evaluate the model by adjusting the Confidence threshold and the confusion matrix.\n<text />Sometimes the training time will be more because of large datasets, node training time as well as infrastructure set up and tear down.\nThough it is cost-efficient because you have to pay for the memory you use, The time processing takes place(for Training the nodes in ML), etc.\n<text />The bottom line is GCP offers IaaS (Infrastructure as a Service) in the form of Google Compute Engine, and it offers Paas (Platform as a Service) in the form of Google App Engine. As for FaaS (Function as a Service), GCP offers it in the form of Google Cloud Functions."
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