Get started with Vertex AI
TLDRIn this video, Jeevana Hectic and Hussein Jiva introduce Google Cloud's Vertex AI, detailing its machine learning offerings. They discuss the four broad categories of ML on Google Cloud, including ML APIs, AutoML, custom model training, and BigQuery ML. The video delves into the benefits of AutoML, showcasing its ability to streamline the ML workflow with pre-built models and significant automation. A hands-on demo illustrates the process of creating a dataset and training a model, highlighting Vertex AI's unified platform for building, deploying, and managing ML workloads. The video concludes with a customer success story featuring Fertile Medicine, emphasizing the platform's role in enhancing diagnostic reliability and expediting deployment cycles.
Takeaways
- 🌟 Google Cloud offers a range of machine learning solutions designed to help startups build and grow their businesses sustainably.
- 🚀 The four broad categories of machine learning offerings on Google Cloud include ML APIs, AutoML, custom model training, and BigQuery ML.
- 📊 ML APIs are best for quick starts with minimal effort and no customizability, making them ideal for those who want to get started fast without much technical setup.
- 🤖 AutoML is suitable for businesses needing more customization and are willing to invest time and effort. It supports various data types like text, images, and videos.
- 🛠️ Custom model training is for teams with ML experts who want a highly customizable platform for specific needs. This option allows for custom building, training, and deployment on a unified platform.
- 📊 BigQuery ML enables the creation of descriptive or predictive ML models using simple SQL queries, even at petabyte scale.
- 🔧 Vertex AI is an umbrella of machine learning products and services that streamline the ML workflow from data readiness to deployment and management.
- 📈 AutoML simplifies the traditional ML workflow by pre-building models, processing data, and handling model selection, feature engineering, and hyperparameter tuning, reducing the time from data to value.
- 🏗️ Custom modeling is for complex use cases that can't be solved with other ML options, offering complete control over model building, training, and deployment.
- 📊 The demo showcased how easy it is to create datasets, train models, and deploy them on Vertex AI, emphasizing the platform's user-friendly interface and powerful capabilities.
- 🌐 The success story of FertiliMedicina, a Brazilian digital healthcare startup, highlights how Vertex AI and Google Cloud can significantly improve ML diagnostic reliability and reduce deployment cycles and costs.
Q & A
What is the main focus of the video?
-The main focus of the video is to introduce and explain the various machine learning offerings on Google Cloud, specifically diving into Vertex AI, its AutoML and custom modeling capabilities, and how they can help startups build and grow their businesses successfully and sustainably.
What are the four broad categories of machine learning offerings on Google Cloud?
-The four broad categories of machine learning offerings on Google Cloud are ML APIs, Vertex AI (including AutoML and custom model training), and BigQuery ML.
How does Vertex AI help streamline the machine learning workflow?
-Vertex AI streamlines the machine learning workflow by providing an umbrella of products and services that cover everything from data readiness, feature engineering, model training, serving the trained model, understanding and tuning it, to deploying it to edge devices and overall management. It offers a unified platform where different tools are available for specific tasks, making the process more efficient and user-friendly.
What is AutoML and how does it benefit users?
-AutoML (Automated Machine Learning) is a service within Vertex AI that allows users to build and train high-quality machine learning models with minimal effort and time. It automates much of the process, including feature engineering, model selection, and hyperparameter tuning, enabling even non-experts to get from data to value quickly, in weeks or days rather than months.
What types of data does AutoML support?
-AutoML supports various types of data including text, tabular data, images, and videos, making it versatile for different use cases and industries.
How does BigQuery ML (BQML) differ from other machine learning offerings on Google Cloud?
-BigQuery ML (BQML) is unique in that it allows users to create descriptive or predictive ML models using simple SQL queries. This is particularly useful for users who have their data already stored in BigQuery data warehouses, as it enables them to leverage their data directly without the need for extensive data movement or manipulation.
What are the benefits of using custom modeling in Vertex AI?
-Custom modeling in Vertex AI is designed for complex and niche use cases that cannot be solved with other ML offerings. It provides the flexibility to define your instance, choose your framework and version, and train your model using virtual machines with configurations that fit your specific needs. This level of customization allows for tailored solutions that can handle intricate problems and provide more accurate results.
How does the video demonstrate the ease of creating a dataset and training a model in Vertex AI?
-The video demonstrates the ease of creating a dataset and training a model in Vertex AI by walking through the process of selecting a data type, uploading files, labeling them, and splitting the data for training and testing. It also shows how to create a model, choose a training method, and start the training process with just a few clicks, highlighting the platform's user-friendly interface and streamlined workflows.
What is the significance of the customer success story featured in the video?
-The customer success story of Fertile medicina, a Brazilian digital healthcare startup, illustrates the real-world impact of using Google Cloud's machine learning offerings. By utilizing Vertex AI and TensorFlow, Fertile medicina improved ML diagnostic reliability from 68% to 90%, reduced diagnosis times from two weeks to 20-30 minutes, and decreased costs by 20% compared to previous cloud providers. This story emphasizes the value and effectiveness of Google Cloud's ML services in enhancing businesses and solving complex problems.
What additional resources are available for those interested in learning more about Vertex AI?
-For those interested in learning more about Vertex AI, the video encourages viewers to click on the links in the description to access more information, explore the AI Simplified YouTube playlist for guided tutorials, and reach out for further connections and insights. These resources provide a comprehensive learning experience and practical guidance for users at all levels of ML expertise.
How does the video conclude and what can viewers expect in the next session?
-The video concludes by encouraging viewers to leverage the powerful AI portfolio on Google Cloud to create their own solutions and magic. It also teases the next video session, which will focus on understanding API Management in detail, including what APIs are, the need for API management, and the choice between API Gateway and Apigee.
Outlines
🚀 Introduction to Technical Series and Google Cloud ML Offerings
The video begins with an introduction to the technical series for startups, focusing on machine learning and artificial intelligence. Jeevana Hecti and Hussein Jiva, as hosts, emphasize the goal of helping startups build and grow their businesses on Google Cloud. They review the previous video on machine learning APIs and introduce the broader categories of machine learning offerings on Google Cloud. The importance of understanding priorities in terms of speed, effort, complexity, and customizability is highlighted. The video outlines the different categories of ML options available, including ML APIs for quick starts, Vertex AI for more customization, and BigQuery ML for creating ML models using SQL queries. The aim is to guide startups in transforming into AI-driven companies.
📊 Understanding AutoML and Custom Modeling in Vertex AI
This paragraph delves into the specifics of AutoML and custom modeling within Vertex AI. It explains the traditional ML workflow and how AutoML streamlines the process by providing pre-built models that require minimal effort from the user. The benefits of using AutoML, such as significant automation and a codeless interface, are discussed. The paragraph also covers custom modeling for complex use cases, where teams with ML expertise can build and train models using various pre-built containers or custom configurations. A brief mention of a customer success story involving Fertile Medica, a Brazilian digital healthcare company, is provided, highlighting the improvements they achieved using Vertex AI and TensorFlow.
🛠️ Demonstration of Custom Training and AutoML on Vertex AI
The host demonstrates how to use Vertex AI for both custom training and AutoML. They walk through the process of creating a dataset, selecting the data type, and uploading files. The video shows how to label files, choose data splits, and create a model. The simplicity of starting machine learning projects with AutoML is emphasized. The demonstration then moves on to custom training, explaining the steps for selecting a training container, setting up hyperparameter tuning, and choosing machine types. The capabilities of Vertex AI are showcased by deploying a pre-trained AutoML model to classify X-ray images and determine whether a patient has pneumonia.
🎉 Conclusion and Future Topics
The video concludes with a recap of the various machine learning offerings of Google Cloud and the detailed exploration of AutoML and custom models in Vertex AI. The hands-on demo and customer success story are highlighted as key takeaways. The hosts encourage viewers to learn more about Vertex AI through provided links, explore AI Simplified YouTube playlists, and engage with the platform through guided tutorials. They also tease the next video topic, which will cover API Management, including understanding APIs, the need for API management, and the choice between API Gateway and Apigee.
Mindmap
Keywords
💡Machine Learning
💡Artificial Intelligence
💡Google Cloud
💡Vertex AI
💡AutoML
💡Custom Modeling
💡BigQuery ML
💡Data Types
💡Model Training
💡Hyperparameter Tuning
💡Deployment
Highlights
Google Cloud offers a variety of machine learning solutions for startups.
Understanding your organization's priorities in terms of speed, effort, and customizability is key to selecting the right machine learning option.
Vertex AI is an umbrella of machine learning products and services for building, training, deploying, managing, and scaling your machine learning workloads.
AutoML in Vertex AI simplifies the traditional ML workflow by automating major parts of the process, such as model architecture selection and parameter tuning.
Custom modeling in Vertex AI allows ML experts to build and train models for complex, niche use cases on a highly customizable platform.
BigQuery ML (BQML) enables the creation of descriptive or predictive ML models using simple SQL queries on data stored in BigQuery data warehouses.
Vertex AI provides a unified experience with integrated options for ML APIs, AutoML, custom model training, and BigQuery ML.
AutoML's codeless interface guides users through the ML lifecycle with significant automation and safeguards at each step.
Google's Model Zoo includes a wide range of techniques from feed forward DNNs to gradient boosters and decision trees.
Custom training in Vertex AI allows for the use of pre-built containers or custom containers, and the deployment of models through Cloud Endpoints or Batch AI.
Vertex AI's robust, secure foundation provides a seamless experience and flexibility for users of all ML expertise levels.
In the demo, creating a dataset and model in Vertex AI is shown to be straightforward, with options for data labeling and split.
Hyperparameter tuning can be performed within Vertex AI, optimizing model performance by adjusting various parameters.
Deploying a trained model to an endpoint for testing is simple and can be done with a single click in Vertex AI.
FertilMedicina, a Brazilian digital healthcare startup, has improved ML diagnostic reliability and decreased deployment cycles by using Vertex AI and TensorFlow.
The session concluded with a review of various machine learning offerings of Google Cloud, insights into AutoML and custom models in Vertex AI, a hands-on demo, and a customer success story.