Vertex AI
1. Vertex AI
SPEAKER: Before exploring various options to build an ML model, you first need to familiarize yourself with the playground, Vertex AI, Google's unified AI development platform. For years now, Google has invested time and resources into developing data and AI, and applied these technologies to many of its products and services like Gmail, Google Maps, Google Photos, and Google Translate, just to name a few. But developing these technologies doesn't come without challenges. There are challenges around getting ML models into production, for example, scalability, monitoring and continuous integration, delivery and training. In fact, according to Gartner, only half of enterprise ML projects get past the pilot phase. There are also ease-of-use challenges. Many tools on the market require advanced coding skills, which can take a data scientist's focus away from model configuration. Without a unified workflow, data scientists often have difficulties finding tools. Google's solution to many of the production and ease-of-use challenges is Vertex AI, a unified platform that brings all the components of the machine learning ecosystem and workflow together. So what exactly does a unified platform mean? There are two primary aspects. Firstly, it means that Vertex AI provides an end-to-end ML pipeline to prepare data and create, deploy, and manage models over time and at scale. For instance, during the data readiness stage, users can upload data from wherever it's stored, Cloud Storage, BigQuery, or a local machine. Then, during the feature readiness stage, users can create features, which are the processed data that will be put into the model. Then they can share them with others by using the feature store. After that, it's time for training and hyperparameter tuning. This means that when the data is ready, users can experiment with different models and adjust hyperparameters. And finally, during deployment and model monitoring, users can set up the pipeline to transform the model into production by automatically monitoring and performing continuous improvements. You'll learn how to do this later in the course when you explore MLOps. Second, Vertex AI is a unified platform that encompasses both generative AI, enabling creation of multimodal content, and predictive AI, allowing for forecasting and classification. You already explored Gen AI tools in the previous module like Vertex AI Studio and Agent Builder. Let's focus on the tools for predictive AI. Vertex AI allows users to build ML models with either AutoML, a no-code solution, or custom training, a code-based solution. AutoML provides a UI that is easy to navigate. It lets data scientists focus on what business problems to solve, instead of how to code and deploy an ML solution. Custom training gives data scientists and ML engineers more control over the development environment and process. They can use tools like Vertex AI Workbench and Colab to do their ML projects themselves. One convenient feature is that data scientists can now write SQL with Workbench on Vertex AI to seamlessly connect BigQuery and Vertex AI. Being able to perform such a wide range of tasks in one unified platform has many benefits. This can be summarized with four Ss. It's seamless. Vertex AI provides a smooth user experience from uploading and preparing data all the way to model training and production. It's scalable. The Machine Learning Operations, MLOps, provided by Vertex AI help to monitor and manage the ML production, and therefore scale the storage and computing power automatically. It's sustainable. All of the artifacts and features created using Vertex AI can be reused and shared. And it's speedy. Vertex AI produces models that have 80% fewer lines of code than competitors. Let's take a deep dive into ML model building options in the next lesson.2. Let's practice!
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