We’ve designed our new guide for practitioners looking to use Google Cloud’s capabilities across BigQuery, Vertex AI, and Google Cloud Serverless for Apache Spark. We’ll show you how to use an AI-first approach in your data science workflows, use previously untapped unstructured and multimodal data, and achieve new levels of efficiency.
Let’s take a look at what’s inside!
Unified, streamlined workflows
The first section of the ebook explores how Google Cloud helps reduce friction in the data science workflow. We highlight the shift toward a central workspace that blends SQL, Python, and Spark code in the same notebook. Tools like Colab Enterprise notebooks are integrated with assistive features that help you generate multi-step plans and code from high-level goals, allowing you to focus on analysis and interpretation.
End-to-end tooling on a unified data foundation
The ebook also details how a unified data foundation is key to modern data science. With Google Cloud, you can manage structured and unstructured data in one place, and use familiar SQL syntax to process documents or analyze images directly. The guide walks through a spectrum of model development options on the platform – whether you prefer SQL with BigQuery Machine Learning, the flexibility of Spark, a pandas and scikit-learn syntax with BigQuery DataFrames, or custom model development with Vertex AI.
Real-world use cases and code
A significant portion of this ebook is dedicated to common, real-world data science use cases we typically see customers face. These examples show how to tackle projects like modernizing retail demand forecasts or assessing environmental risks for agricultural investments. Each use case comes with a link to a notebook, so you can get started right away.
Download the ebook today!
We hope this ebook will serve as a useful, hands-on resource to improve your data science workflows.
Download your free copy: A Practical Guide to Data Science with Google Cloud.
