Getting Started with Google Cloud Platform in 5 Steps
Explore the essentials of Google Cloud Platform for data science and ML, from account setup to model deployment, with hands-on project examples.
Introduction to Google Cloud Platform
This article aims to provide a step-by-step overview of getting started with Google Cloud Platform (GCP) for data science and machine learning. We'll give an overview of GCP and its key capabilities for analytics, walk through account setup, explore essential services like BigQuery and Cloud Storage, build a sample data project, and use GCP for machine learning. Whether you're new to GCP or looking for a quick refresher, read on to learn the basics and hit the ground running with Google Cloud.
What is GCP?
Google Cloud Platform offers a whole range of cloud computing services to help you build and run apps on Google's infrastructure. For computing power, there's Compute Engine that lets you spin up virtual machines. If you need to run containers, Kubernetes does the job. BigQuery handles your data warehousing and analytics needs. And with Cloud ML, you get pre-trained machine learning models via API for things like vision, translation and more. Overall, GCP aims to provide the building blocks you need so you can focus on creating great apps without worrying about the underlying infrastructure.
Benefits of GCP for Data Science
GCP offers several benefits for data analytics and machine learning:
- Scalable compute resources that can handle big data workloads
- Managed services like BigQuery to process data at scale
- Advanced machine learning capabilities like Cloud AutoML and AI Platform
- Integrated analytics tools and services
How GCP Compares to AWS and Azure
Compared to Amazon Web Services and Microsoft Azure, GCP stands out with its strength in big data, analytics and machine learning, and its offer of managed services like BigQuery and Dataflow for data processing. The AI Platform makes it easy to train and deploy ML models. Overall GCP is competitively priced and a top choice for data-driven applications.
|Feature||Google Cloud Platform (GCP)||Amazon Web Services (AWS)||Microsoft Azure|
|Pricing*||Competitive pricing with sustained use discounts||Per-hour pricing with reserved instance discounts||Per-minute pricing with reserved instance discounts|
|Data Warehousing||BigQuery||Redshift||Synapse Analytics|
|Machine Learning||Cloud AutoML, AI Platform||SageMaker||Azure Machine Learning|
|Compute Services||Compute Engine, Kubernetes Engine||EC2, ECS, EKS||Virtual Machines, AKS|
|Serverless Offerings||Cloud Functions, App Engine||Lambda, Fargate||Functions, Logic Apps|
*Note that the pricing models are necessarily simplified for our purposes. AWS and Azure also offer sustained use or committed use discounts similar to GCP; pricing structures are complex and can vary significantly based on a multitude of factors, so the reader is encouraged to look further into this themselves to determine what the actual costs could be in their situation.
In this table, we've compared Google Cloud Platform, Amazon Web Services, and Microsoft Azure based on various features such as pricing, data warehousing, machine learning, compute services, and serverless offerings. Each of these cloud platforms has its own unique set of services and pricing models, which cater to different business and technical requirements.
Step 1: Setting Up Your GCP Account
Creating a Google Cloud Account
To use GCP, first sign up for a Google Cloud account. Go to the homepage and click on "Get started for free". Follow the prompts to create your account using your Google or Gmail credentials.
Creating a Billing Account
Next you'll need to set up a billing account and payment method. This allows you to use paid services beyond the free tier. Navigate to the Billing section in the console and follow prompts to add your billing information.
Understanding GCP Pricing
GCP offers a generous 12-month free tier with $300 credit. This allows usage of key products like Compute Engine, BigQuery and more at no cost. Review pricing calculators and docs to estimate full costs.
Install Google Cloud SDK
Install the Cloud SDK on your local machine to manage projects/resources via command line. Download from the Cloud SDK guide page and follow the install guide.
Finally, be sure to have a look at and keep handy the Get Started with Google Cloud documentation.
Step 2: GCP Services for Data Science
Google Cloud Platform (GCP) is laden with a myriad of services designed to cater to a variety of data science needs. Here, we delve deeper into some of the essential services like BigQuery, Cloud Storage, and Cloud Dataflow, shedding light on their functionality and potential use cases.
BigQuery stands as GCP's fully managed, low cost analytics database. With its serverless model, BigQuery enables super-fast SQL queries against append-mostly tables, by employing the processing power of Google's infrastructure. It is not just a tool for running queries, but a robust, large-scale data warehousing solution, capable of handling petabytes of data. The serverless approach eradicates the need for database administrators, making it an attractive option for enterprises looking to reduce operational overheads.
Example: Delving into the public natality dataset to fetch insights on births in the US.
SELECT * FROM `bigquery-public-data.samples.natality` LIMIT 10
Cloud Storage allows for robust, secure and scalable object storage. It's an excellent solution for enterprises as it allows for the storage and retrieval of large amounts of data with a high degree of availability and reliability. Data in Cloud Storage is organized into buckets, which function as individual containers for data, and can be managed and configured separately. Cloud Storage supports standard, nearline, coldline, and archive storage classes, allowing for the optimization of price and access requirements.
Example: Uploading a sample CSV file to a Cloud Storage bucket using the gsutil CLI.
gsutil cp sample.csv gs://my-bucket
Cloud Dataflow is a fully managed service for stream and batch processing of data. It excels in real-time or near real-time analytics and supports Extract, Transform, and Load (ETL) tasks as well as real-time analytics and artificial intelligence (AI) use cases. Cloud Dataflow is built to handle the complexities of processing vast amounts of data in a reliable, fault-tolerant manner. It integrates seamlessly with other GCP services like BigQuery for analysis and Cloud Storage for data staging and temporary results, making it a cornerstone for building end-to-end data processing pipelines.
Step 3: Building Your First Data Project
Embarking on a data project necessitates a systematic approach to ensure accurate and insightful outcomes. In this step, we’ll walk through creating a project on Google Cloud Platform (GCP), enabling the necessary APIs, and setting the stage for data ingestion, analysis, and visualization using BigQuery and Data Studio. For our project, let’s delve into analyzing historical weather data to discern climate trends.
Set up Project and Enable APIs
Kickstart your journey by creating a new project on GCP. Navigate to the Cloud Console, click on the project drop-down and select “New Project.” Name it “Weather Analysis” and follow through the setup wizard. Once your project is ready, head over to the APIs & Services dashboard to enable essential APIs like BigQuery, Cloud Storage, and Data Studio.
Load Dataset into BigQuery
For our weather analysis, we’ll need a rich dataset. A trove of historical weather data is available from NOAA. Download a portion of this data and head over to the BigQuery Console. Here, create a new dataset named `weather_data`. Click on “Create Table”, upload your data file, and follow the prompts to configure the schema.
Table Name: historical_weather Schema: Date:DATE, Temperature:FLOAT, Precipitation:FLOAT, WindSpeed:FLOAT
Query Data and Analyze in BigQuery
With data at your disposal, it's time to unearth insights. BigQuery’s SQL interface makes it seamless to run queries. For instance, to find the average temperature over the years:
SELECT EXTRACT(YEAR FROM Date) as Year, AVG(Temperature) as AvgTemperature FROM `weather_data.historical_weather` GROUP BY Year ORDER BY Year ASC;
This query avails a yearly breakdown of average temperatures, crucial for our climate trend analysis.
Visualize Insights with Data Studio
Visual representation of data often unveils patterns unseen in raw numbers. Connect your BigQuery dataset to Data Studio, create a new report, and start building visualizations. A line chart showcasing temperature trends over the years would be a good start. Data Studio’s intuitive interface makes it straightforward to drag, drop and customize your visualizations.
Share your findings with your team using the "Share" button, making it effortless for stakeholders to access and interact with your analysis.
By following through this step, you've set up a GCP project, ingested a real-world dataset, executed SQL queries to analyze data, and visualized your findings for better understanding and sharing. This hands-on approach not only helps in comprehending the mechanics of GCP but also in gaining actionable insights from your data.
Step 4: Machine Learning on GCP
Utilizing machine learning (ML) can substantially enhance your data analysis by providing deeper insights and predictions. In this step, we'll extend our "Weather Analysis" project, employing GCP's ML services to predict future temperatures based on historical data. GCP offers two primary ML services: Cloud AutoML for those new to ML, and AI Platform for more experienced practitioners.
Overview of Cloud AutoML and AI Platform
- Cloud AutoML: This is a fully managed ML service that facilitates the training of custom models with minimal coding. It's ideal for those without a deep machine learning background.
- AI Platform: This is a managed platform for building, training, and deploying ML models. It supports popular frameworks like TensorFlow, scikit-learn, and XGBoost, making it suitable for those with ML experience.
Hands-on Example with AI Platform
Continuing with our weather analysis project, our goal is to predict future temperatures using historical data. Initially, the preparation of training data is a crucial step. Preprocess your data to a format suitable for ML, usually CSV, and split it into training and test datasets. Ensure the data is clean, with relevant features selected for accurate model training. Once prepared, upload the datasets to a Cloud Storage bucket, creating a structured directory like
Training a model is the next significant step. Navigate to the AI Platform on GCP and create a new model. Opt for a pre-built regression model, as we are predicting a continuous target—temperature. Point the model to your training data in Cloud Storage and set the necessary parameters for training. GCP will automatically handle the training process, tuning, and evaluation, which simplifies the model building process.
Upon successful training, deploy the trained model within AI Platform. Deploying the model allows for easy integration with other GCP services and external applications, facilitating the utilization of the model for predictions. Ensure to set the appropriate versioning and access controls for secure and organized model management.
Now with the model deployed, it's time to test its predictions. Send query requests to test the model's predictions using the GCP Console or SDKs. For instance, input historical weather parameters for a particular day and observe the predicted temperature, which will give a glimpse of the model's accuracy and performance.
Hands-on with Cloud AutoML
For a more straightforward approach to machine learning, Cloud AutoML offers a user-friendly interface for training models. Start by ensuring your data is appropriately formatted and split, then upload it to Cloud Storage. This step mirrors the data preparation in the AI Platform but is geared towards those with less ML experience.
Proceed to navigate to AutoML Tables on GCP, create a new dataset, and import your data from Cloud Storage. This setup is quite intuitive and requires minimal configurations, making it a breeze to get your data ready for training.
Training a model in AutoML is straightforward. Select the training data, specify the target column (Temperature), and initiate the training process. AutoML Tables will automatically handle feature engineering, model tuning, and evaluation, which lifts the heavy lifting off your shoulders and allows you to focus on understanding the model’s output.
Once your model is trained, deploy it within Cloud AutoML and test its predictive accuracy using the provided interface or by sending query requests via GCP SDKs. This step brings your model to life, allowing you to make predictions on new data.
Lastly, evaluate your model's performance. Review the model’s evaluation metrics, confusion matrix, and feature importance to understand its performance better. These insights are crucial as they inform whether there's a need for further tuning, feature engineering, or gathering more data to improve the model's accuracy.
By immersing in both the AI Platform and Cloud AutoML, you gain a practical understanding of harnessing machine learning on GCP, enriching your weather analysis project with predictive capabilities. Through these hands-on examples, the pathway to integrating machine learning into your data projects is demystified, laying a solid foundation for more advanced explorations in machine learning.
Step 5: Deploying Models to Production
Once your machine learning model is trained to satisfaction, the next crucial step is deploying it to production. This deployment allows your model to start receiving real-world data and return predictions. In this step, we'll explore various deployment options on GCP, ensuring your models are served efficiently and securely.
Serving Predictions via Serverless Services
Serverless services on GCP like Cloud Functions or Cloud Run can be leveraged to deploy trained models and serve real-time predictions. These services abstract away infrastructure management tasks, allowing you to focus solely on writing and deploying code. They are well-suited for intermittent or low-volume prediction requests due to their auto-scaling capabilities.
For instance, deploying your temperature prediction model via Cloud Functions involves packaging your model into a function, then deploying it to the cloud. Once deployed, Cloud Functions automatically scales up or down as many instances as needed to handle the rate of incoming requests.
Creating Prediction Services
For high-volume or latency-sensitive predictions, packaging your trained models in Docker containers and deploying them to Google Kubernetes Engine (GKE) is a more apt approach. This setup allows for scalable prediction services, catering to a potentially large number of requests.
By encapsulating your model in a container, you create a portable and consistent environment, ensuring it will run the same regardless of where the container is deployed. Once your container is ready, deploy it to GKE, which provides a managed Kubernetes service to orchestrate your containerized applications efficiently.
Deploying models to production also involves adhering to best practices to ensure smooth operation and continued accuracy of your models.
- Monitor Models in Production: Keep a close eye on your model's performance over time. Monitoring can help detect issues like model drift, which occurs when the model's predictions become less accurate as the underlying data distribution changes.
- Regularly Retrain Models on New Data: As new data becomes available, retrain your models to ensure they continue to make accurate predictions.
- Implement A/B Testing for Model Iterations: Before fully replacing an existing model in production, use A/B testing to compare the performance of the new model against the old one.
- Handle Failure Scenarios and Rollbacks: Be prepared for failures and have a rollback plan to revert to a previous model version if necessary.
Optimizing for Cost
Cost optimization is vital for maintaining a balance between performance and expenses.
- Use Preemptible VMs and Autoscaling: To manage costs, utilize preemptible VMs which are significantly cheaper than regular VMs. Combining this with autoscaling ensures you have necessary resources when needed, without over-provisioning.
- Compare Serverless vs Containerized Deployments: Assess the cost differences between serverless and containerized deployments to determine the most cost-effective approach for your use case.
- Right-size Machine Types to Model Resource Needs: Choose machine types that align with your model's resource requirements to avoid overspending on underutilized resources.
Securing your deployment is paramount to safeguard both your models and the data they process.
- Understand IAM, Authentication, and Encryption Best Practices: Familiarize yourself with Identity and Access Management (IAM), and implement proper authentication and encryption to secure access to your models and data.
- Secure Access to Production Models and Data: Ensure only authorized individuals and services have access to your models and data in production.
- Prevent Unauthorized Access to Prediction Endpoints: Implement robust access controls to prevent unauthorized access to your prediction endpoints, safeguarding your models from potential misuse.
Deploying models to production on GCP involves a mixture of technical and operational considerations. By adhering to best practices, optimizing costs, and ensuring security, you lay a solid foundation for successful machine learning deployments, ready to provide value from your models in real-world applications.
In this comprehensive guide, we have traversed the essentials of kickstarting your journey on Google Cloud Platform (GCP) for machine learning and data science. From setting up a GCP account to deploying models in a production environment, each step is a building block towards creating robust data-driven applications. Here are the next steps to continue your exploration and learning on GCP.
- GCP Free Tier: Take advantage of the GCP free tier to further explore and experiment with the cloud services. The free tier provides access to core GCP products and is a great way to get hands-on experience without incurring additional costs.
- Advanced GCP Services: Delve into more advanced GCP services like Pub/Sub for real-time messaging, Dataflow for stream and batch processing, or Kubernetes Engine for container orchestration. Understanding these services will broaden your knowledge and skills in managing complex data projects on GCP.
- Community and Documentation: The GCP community is a rich source of knowledge, and the official documentation is comprehensive. Engage in forums, attend GCP meetups, and explore tutorials to continue learning.
- Certification: Consider pursuing a Google Cloud certification, such as the Professional Data Engineer or Professional Machine Learning Engineer, to validate your skills and enhance your career prospects.
- Collaborate on Projects: Collaborate on projects with peers or contribute to open-source projects that utilize GCP. Real-world collaboration provides a different perspective and enhances your problem-solving skills.
The tech sphere, especially cloud computing and machine learning, is continually evolving. Staying updated with the latest advancements, engaging with the community, and working on practical projects are excellent ways to keep honing your skills. Moreover, reflect on completed projects, learn from any challenges faced, and apply those learnings to future endeavors. Each project is a learning opportunity, and continual improvement is the key to success in your data science and machine learning journey on GCP.
By following this guide, you've laid a robust foundation for your adventures on Google Cloud Platform. The road ahead is filled with learning, exploration, and ample opportunities to make significant impacts with your data projects.
Matthew Mayo (@mattmayo13) holds a Master's degree in computer science and a graduate diploma in data mining. As Editor-in-Chief of KDnuggets, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.