You may have heard about Google’s cloud computing platform, but have you considered using it for data science? Google Cloud Platform is an end-to-end data pipeline for collaborative data science. It can even be used for a variety of statistical and machine learning methods. This course will walk you through some of the steps to use the cloud computing platform to do data science. To learn more, download the free PDF. There are five sections in this course: Description, Overview, Building models, and Hands-on project.
Description
Data Science on the Google Cloud Platform is a comprehensive and collaborative tool for creating, running, and analyzing data. Data scientists can apply sophisticated statistical and machine learning techniques to a variety of data sets. The book provides practical examples, code samples, and notebooks for each step in the data science process. This hands-on guide provides the foundation for data scientists to develop and use their own data science tools. However, if you are new to data science, you should first read the book.
While the Google Cloud Platform has many similar building blocks, its business facing dashboards are less robust. Google is dependent on third parties to provide such tools. There are three major providers of cloud analytics. Google, AWS, and Microsoft Azure all provide similar building blocks, but differ in the specific products that they offer. The most basic Google Cloud Platform features include pre-trained machine learning models and managed lab notebooks, but do not include a range of more advanced features.
The services that support the development of large data sets are vast. Google provides cloud storage services for both structured and unstructured data. There are also database storage services available, including Cloud SQL for MySQL relational databases and native Cloud Bigtable for Apache Spark. For managing workloads, the Google Cloud platform also includes Google Compute Engine, which is an infrastructure-as-a-service (IaaS) system for running Docker containers. Google Cloud also includes the Cloud Operations Suite, which was previously known as Stackdriver.
Google Cloud Platform is a great choice for companies looking to build a data science tool. Several of its features include an interactive dashboard, a customizable workspace, and API management. The data science tools available on Google Cloud Platform include Apigee Sense, an API security system that alerts administrators when an API behaves suspiciously. Google Cloud Platform provides 25 regions and 77 zones. A region is a geographical area where a specific service is deployed. Zones are generally considered a single failure domain within a region.
Overview
An overview of data science on the Google Cloud Platform teaches developers how to build and implement end-to-end data pipelines using the Google Cloud Platform. This course covers a broad range of topics including data pipelines, machine learning, and data processing systems. Students will also learn how to analyze both structured and unstructured data. The course covers both the GCP and the internal tools used for this type of work.
Once a Data Scientist has mastered BigQuery, the next step is to learn how to use Cloud Dataprep, a serverless tool managed by Trifacta. Cloud Dataprep allows users to quickly explore and clean large amounts of data. Cloud Dataproc allows users to run Apache Spark jobs, while Cloud Datafusion and Cloud Composer orchestrate work between GCP services. Cloud SQL also provides a relational data lake for BigQuery, so developers don’t need to worry about data management.
BigQuery is an enterprise-grade data warehouse powered by Google infrastructure. Its capabilities include massive datasets, high performance SQL queries, and machine learning. BigQuery enables customers to maintain control of data and can provide limited or full control of their data. BigQuery ML is an analytics tool that lets users train and deploy machine learning models directly within the platform. The API supports semi-structured and planet-scale structured data, as well as text-based text. The Cloud Prediction API sits in the middle, allowing users to train regression or categorical models on the Google Cloud Platform.
The Google Cloud Platform offers an overview of the building blocks used in data science. The main building blocks used in this area include machine learning and data warehouses. Looker also offers a wide range of business-facing analytics tools, such as visualizations and dashboards. Machine learning unlocks the value in data, and the model development stage is a key part of this. A key theme in this phase is experimentation, with data scientists looking for ways to accelerate iteration speed and reduce infrastructure overhead.
Building models
Data Science on the Google Cloud Platform is a high-level quest that covers data ingestion, preparation, processing, and querying. This book also covers the fundamentals of data science programming and visualization. It also includes examples of how to apply machine learning concepts and methods to data. By the end of the book, you will be fully equipped to tackle the challenges you face in implementing big data analytics in your organization.
Google Cloud Platform provides computational resources to help the public design and train cutting-edge machine learning and deep-learning models. As machine learning and deep learning are rapidly evolving fields, many beginners struggle to get started. This book walks you through the entire process from the fundamentals to advanced analytics on GCP. It also includes the practical skills required to develop machine learning solution pipelines. It includes case studies from real-world projects to showcase the techniques that can be used to solve complex problems.
Google Cloud Platform AI offers a wide range of cloud services for businesses and individuals. You can use its REST APIs and pre-trained models to train custom machine learning models. You can also use its video intelligence services to create custom video models. You can even use its Cloud Natural Language API to extract text from documents and speech recordings. This platform also provides Cloud Translate API to translate text from one language to another.
The main goal of this end-to-end project is to demonstrate how to build a large-scale learning model on GCP. Figure 44-1 depicts the model architecture on GCP. Exploratory data analysis occurs on GCS and large-scale data processing occurs on the GCP. Then, the trained model is deployed to the Cloud MLE. Finally, the end result of this project is a predictive model.
Hands-on project
A hands-on project for data science on the Google Cloud platform will help you learn more about advanced features of the cloud platform. This hands-on project will use the Tensor2Tensor model to predict the titles of news articles based on body text. You’ll learn how to deploy this model using TF Serving and access it through a web app. This project is created by Zack Akil, a Machine Learning Engineer at Google Cloud in London and Developer Advocate for AI on the Google Cloud platform. Lee is an author of the Hands-on Sencha Touch 2 and the Google Cloud platform.
The course begins with a deep dive into machine learning concepts and the mathematical foundations required to understand and implement these models. It then covers deployment, debugging, serving TensorFlow code at scale, and recommendation systems. It concludes with case studies and best practices for working with these models. Regardless of your level of expertise, you’ll gain hands-on experience with these cutting-edge data science platforms and begin creating applications today.
A hands-on project for data science on the Google Cloud Platform will give you a basic understanding of the concepts behind data analysis on the Google Cloud Platform. The course will introduce you to tools like BigQuery, Cloud Data Fusion, and advanced data integration features. You’ll also use the Google Cloud Qwiklab virtual environment to perform your project. It’s a good opportunity to try out a variety of tools and applications and see which one suits your needs best.
In this hands-on project, you’ll be working with the Google Cloud Platform to solve a problem in the shipping industry. Using the Google Cloud Platform’s storage services and analytics tools to process videos is a great way to tackle these challenges. The use of Google’s Machine Learning (ML) platforms, including Apache Spark, Scalding, and Kiji, can make it easier to work with your data.
