Introduction of Google Cloud Platform

Cloud computing, as everyone knows, is an invention made for the convenience of numerous organizations and individuals so that they don’t have to manage physical servers or even run software applications on their machines. One can also access files and applications from any device they need as the storage is on servers in data centres in the place of a user device. 

Google Cloud Platform for that matter, is a public cloud platform that offers various handy services which are provided by Google. Google Cloud platform offers a comprehensive collection of hosted services for computing, storage as well and application development that runs on Google hardware. 

Some of the key features of the Google Cloud Platform are : 

  • Google compute engine 
  • Google app engine
  • Google cloud storage 
  • Google container engine 

If one wishes to get the Google Cloud digital leader certification or become the Google Cloud digital leader, one can do this by learning about all the aspects of the GCP. A Google Cloud digital leader has to undertake some responsibilities related to the Google Cloud core products, and services and how they can benefit organizations and individuals. A Google Cloud digital leader certification is worth it as it can bring noticeable improvements to your career. Having a Google Cloud certification will help you get highlighted in the crowd and will make recruiters hire you first. The Google Cloud training certification displays your skills and experience in the very field that will assist you in getting better job opportunities. 

Benefits of the Google Cloud Platform :

  • Scalable compute resources that manage big data workloads
  • Handled services like BigQuery which assists in processing data at scale
  • Enhanced machine learning capabilities namely, Cloud AutoML as well as AI platform 
  • Integrated analytics tools and services 

How to get started with GCP :

Step 1 – Setting Up Your GCP Account

  • Creating a Google Cloud Account
  • Make a Billing Account 
  • Getting to know the Google Cloud Platform Pricing 
  • Get Google Cloud SDK

Step 2 – GCP Services

Google Cloud Platform (GCP) is rich in advantageous services for the fulfilment of the needs of all users. By getting the Google Cloud platform certification, one can learn about all these aspects and services.

  • BigQuery
  • Cloud Storage
  • Cloud Dataflow

Step 3 – Creating a Data Project

  • Setting up a data project and enabling the APIs
  • Loading up the dataset into BigQuery
  • Query Data and Analyzing in BigQuery
  • Visualizing insights with the assistance of Data Studio

Step 4 – Machine Learning on the Google Cloud Platform 

Making good use of machine learning (ML) can help in enhancing data analysis by providing deep insight and predictions and here, one can extend their weather analysis project by employing Google Cloud Platform’s Machine learning services which can help in predicting the future temperatures that is purely based in historical data. 

  • What is Cloud AutoML?

Cloud AutoML is a fully handled Machine learning service that helps in the training of custom models with minimal coding. It is favourable for individuals who are not as familiar with machine learning.

  • What is an AI Platform?

AI Platform is a fully managed platform that is utilized for building, training, as well as deploying Machine Learning models. The AI platform supports leading frameworks like TensorFlow, scikit-learn, as well as XGBoost which turns this into an enhanced ML experience.

  • Hands-on example with AI Platform
  • Hands-on with cloud AutoML

Step 5 – Launching models into production 

After your machine learning model is trained to satisfaction, it is then important to deploy production after it. This helps in allowing your model to start getting real-world data and return predictions. Here, one can look into multiple options for deployment on GCP that ensure that the models are efficient and secure.

  • Allowing predictions through serverless services
  • Making prediction services
  • Ideal practices – monitoring models in production, frequently retraining models on new data, implementing A/B testing for model iterations, managing failure scenarios as well as rollbacks.
  • Cost Optimization – utilizing preemptible VMs as well as autoscaling, the contrast between serverless and containerized deployments, accurately sized machine types to model resource needs, etc.
  • Security conditions like understanding the IAM and authentication, securing access for production models and data and avoiding unauthorized access to endpoints.


Here, we have mentioned the five steps which are required to get started with the Google Cloud Platform which is essential for machine learning and data science. One can also explore more options to continue this process like : 

  • GCP Free Tier
  • Advanced GCP Services
  • Community and Documentation 
  • Certification
  • Collaboration on Projects