Welcome to the Google Cloud big data and machine learning fundamentals course. My name is Marcus. I'm katelyn. We're from the Google Cloud team. We will be leading you through this course. It's an exciting time to be exploring big data, artificial intelligence and machine learning. Innovation in this field is presenting new opportunities that weren't available just a few years ago, and by joining us on this course, we hope you'll be putting yourself in a position to benefit from these technologies. This course provides an introduction to the tools and technologies Google Cloud offers to work with large data sets and then integrate that data into the artificial intelligence and machine learning lifecycle. Data and AI have a powerful partnership. Data is the foundation of every application, integrated artificial intelligence. Without data, there's nothing for AI to learn from, no pattern to recognize, and no insight to glean. Conversely, without artificial intelligence, large amounts of data can be unmanageable or underutilized. Google has nine products with over one billion users. Android, Chrome, Gmail, Google Drive, Google Maps, Google search, the Google Play Store, YouTube, and photos. That's a lot of data being processed every day. To meet the needs of a growing user base, Google has developed the infrastructure to ingest, manage, and serve high quantities of data from these applications, and artificial intelligence and machine learning has been integrated into these products to make the user experience of each even more productive. This includes features like search in photos, recommendations in YouTube or smart compose in Gmail. Google Cloud offerings can be broadly categorized as compute, storage, big data, and machine learning services for web, mobile analytics and back-end solutions. The main focus of this course is on big data and machine learning. In the first section, you'll be introduced to big data and machine learning on Google Cloud. This includes Google Cloud's Infrastructure and big data and machine learning products. In the second section of the course, we will explore data engineering for streaming data. This includes how to build a streaming data pipelines from ingestion with Pub Sub to processing with dataflow, and finally to visualization using data studio and look up. After that, you'll explore big data with BigQuery, Google's popular data warehouse tool, and BigQuery ML. The embedded ML functionality used for developing machine learning models directly in BigQuery. From there, you'll compare the four options provided by Google Cloud to build and deploy a machine learning model. In the final section of the course, you'll learn how to build a machine learning workflow from start to finish using vertex AI. A unified platform that brings all the components of the machine learning ecosystem and workflow together. The course includes a mix of videos, quizzes, and hands-on labs. Lamps are hosted in a clean Cloud environment for a fixed period of time. At the end of each section, you'll also find a resources section with links to additional reading material. This course was designed for a wide range of learners. This includes, anyone at an organization involved or interested in the data to AI lifecycle, such as product managers, data analysts, data engineers, data scientists, ML developers, and ML engineers. While you'll be learning about services and concepts that are specific to big data and machine learning in this course, remember that because this is a fundamentals level course, some content will be geared toward learners who are entirely new to Cloud technologies. Although this course has no prerequisites, some knowledge of SQL and basic machine learning concepts will be helpful. You can learn more about where this course fits into the learning path for your specific role and all the training courses offered by Google Cloud, at cloud.google.com/training. Are you ready to learn more about the exciting world of data machine learning? Great, let's get started.