Get started with Vertex AI

Google Cloud Tech
17 Oct 202217:18

TLDRThis video introduces Google Cloud's Vertex AI, a comprehensive suite for machine learning workloads. It outlines four categories of ML offerings, highlights the benefits of AutoML for quick model training, and explores custom model training for complex use cases. A demo illustrates creating a dataset, training a model, and deploying it for image classification, showcasing Vertex AI's capabilities in action.

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.
  • 📊 AutoML is suitable for startups needing customization and is applicable to various data types such as text, images, videos, and tabular data.
  • 🤖 Custom model training in Vertex AI is ideal for teams with ML experts who require a highly customizable platform for specific needs.
  • 🔍 BigQuery ML allows for the creation of descriptive and predictive ML models using simple SQL queries on data stored in BigQuery data warehouses.
  • 🛠️ Vertex AI is a unified platform for ML workflows, providing tools for data readiness, feature engineering, model training, serving, and monitoring.
  • 🌐 The platform offers a range of options from ML APIs for quick starts to custom model training for complex use cases.
  • 📈 AutoML streamlines the traditional ML workflow by automating model selection, feature engineering, and hyperparameter tuning, significantly reducing development time.
  • 🔧 Custom modeling provides pre-built containers for popular ML frameworks like PyTorch, scikit-learn, TensorFlow, and XGBoost, as well as the option for custom containers.
  • 📊 Vertex AI includes tools for hyperparameter tuning, enabling optimization of model performance through various parameters and scaling options.
  • 🌟 A customer success story featuring Fertile Medicine illustrates the benefits of using Vertex AI, including increased ML diagnostic reliability and reduced deployment cycles.

Q & A

  • What are the four broad categories of machine learning offerings on Google Cloud?

    -The four broad categories of machine learning offerings on Google Cloud include ML APIs, Vertex AI in AutoML, Vertex AI with custom model training, and BigQuery ML (BQML).

  • What is the main advantage of using ML APIs for startups?

    -The main advantage of using ML APIs for startups is that they can get started very quickly with minimal effort and without the need for customization.

  • How does AutoML in Vertex AI help users with different types of data?

    -AutoML in Vertex AI assists users by automatically processing the data, choosing the best fit model, and providing predictions, thus requiring less effort and technical expertise from the user, regardless of the data type such as text, image, video, or tabular data.

  • What is the purpose of Vertex AI with custom model training?

    -Vertex AI with custom model training is designed for users with ML expertise who need a highly customizable platform to build and train models for very specific and complex use cases.

  • How does BigQuery ML (BQML) simplify the process of creating ML models?

    -BigQuery ML (BQML) allows users to create descriptive or predictive ML models using simple SQL queries, even with data stored in petabyte-scale BigQuery data warehouses.

  • What does Vertex AI provide as an umbrella of machine learning products and services?

    -Vertex AI provides a unified experience with various options like ML APIs, AutoML, custom model training, and BigQuery ML. It covers the entire ML workflow from data readiness to model deployment and management, offering solutions for each step.

  • How does AutoML accelerate the traditional ML workflow?

    -AutoML accelerates the traditional ML workflow by providing a codeless interface that guides users through the ML life cycle with significant automation. It takes care of feature engineering, model selection, and hyperparameter tuning, thus reducing the time from data to value.

  • What are some of the machine learning techniques included in Google's model portfolio?

    -Google's model portfolio includes feed-forward DNNs, deep and wide neural networks, gradient boosters, decision trees, and combinations of these techniques.

  • How does custom training in Vertex AI differ from AutoML?

    -Custom training in Vertex AI is for more complex and niche use cases. It allows users to define their instances, upload their training data, build their own models, and train them using virtual machines with various configurations or pre-built containers.

  • What is the role of the Google Cloud Console in accessing Vertex AI?

    -The Google Cloud Console is the primary interface for accessing and managing Vertex AI services. Users need the required permissions to access anything on the console, either via the web interface or the command-line interface.

  • How does Vertex AI support model deployment and testing?

    -Vertex AI allows users to deploy their trained models to endpoints and test them, for example, using Cloud Endpoints for online predictions or Batch AI for batch predictions, all from a single platform.

Outlines

00:00

🚀 Introduction to Technical Series for Startups and Google Cloud ML Offerings

The video introduces Jeevana Hecti and Hussein Jiva, specialists discussing technical enablement for startups. They aim to help businesses grow sustainably on Google Cloud. After covering Machine Learning APIs in the previous video, they will now delve into machine learning on Google Cloud, covering four broad categories of offerings. The video emphasizes the importance of understanding one's priorities in terms of speed, effort, complexity, and customizability. They introduce Google Cloud's machine learning options, including ML APIs for quick starts, Vertex AI for more customization, and BigQuery ML for streaming data or creating ML models using SQL queries. The goal is to guide startups in transforming into AI-driven companies.

05:01

📊 Understanding Vertex AI and its Role in ML Workflows

This paragraph explains the concept of Vertex AI, an umbrella term for a suite of machine learning products and services. It covers the entire ML workflow, from data readiness and feature engineering to model training, serving, and management. Vertex AI offers a unified experience with various options like ML APIs, AutoML, custom model training, and BigQuery ML. The platform is built on Google's secure foundation, providing a seamless experience and flexibility for users of all ML expertise levels. The paragraph also introduces the idea of automating the ML lifecycle and the benefits of using Google's Model Zoo, which includes a wide range of techniques from different neural networks to decision trees.

10:01

🤖 Detailed Explanation of AutoML and Custom Modeling in Vertex AI

The paragraph provides a detailed look at AutoML, a feature that simplifies the traditional ML workflow by skipping the model architecture and parameter tuning steps. AutoML offers a codeless interface that automates the ML lifecycle, from defining data schema to training the model and evaluating its performance. It also covers custom modeling for complex use cases, where teams with ML expertise can build and train models using various frameworks and configurations. The paragraph also includes a quick demo of the Vertex AI console, showcasing the process of creating a dataset, uploading files, labeling, and setting up data splits for training and testing.

15:03

🏥 Customer Success Story: Fertile Mdcina and its Use of Vertex AI

The video concludes with a customer success story featuring Fertile Mdcina, a Brazilian digital healthcare startup. Fertile Mdcina uses ML models to evaluate diagnostic exams, classify findings, and notify medical teams. By utilizing Vertex AI and TensorFlow, the company has significantly improved ML diagnostic reliability and reduced the diagnosis time from two weeks to 20-30 minutes. The CEO of Fertile Mdcina praises Google Cloud's ML engine for its flexibility and cost-effectiveness. The video ends with a call to action for viewers to explore Vertex AI further through provided links, watch more tutorials, and engage for more information.

Mindmap

Keywords

💡Machine Learning

Machine Learning is a subset of Artificial Intelligence that focuses on the development of computer programs that can access data and learn from it. In the context of the video, it is the core technology that enables startups to build intelligent systems that can make decisions, predictions, or classifications based on data inputs. The video discusses various offerings on Google Cloud that facilitate Machine Learning, such as APIs, Vertex AI, and BigQuery ML.

💡Artificial Intelligence

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and act like humans. In the video, AI is the broader field under which Machine Learning falls. AI technologies are used to create systems that can perform tasks that would normally require human intelligence, such as speech recognition, visual perception, decision-making, and language translation.

💡Google Cloud

Google Cloud is a suite of cloud computing services offered by Google, which includes a variety of hosted solutions for compute, data storage, and networking. In the video, Google Cloud is presented as a platform that provides a range of machine learning services and tools, such as Vertex AI and BigQuery ML, to help startups build, train, and deploy machine learning models efficiently.

💡Vertex AI

Vertex AI is an integrated suite of machine learning products and services offered by Google Cloud, designed to streamline the process of building, training, deploying, managing, and scaling machine learning models. It provides a unified platform for data readiness, feature engineering, model training, serving the trained model, and monitoring its performance. In the video, Vertex AI is highlighted as a key offering that simplifies the machine learning workflow for startups.

💡AutoML

AutoML, or Automated Machine Learning, is a technology that enables the process of machine learning model creation to be automated. It simplifies the development of models by automatically selecting the best model architecture, building the model, and tuning its parameters without the need for extensive machine learning expertise. In the video, AutoML is presented as a way for startups to quickly develop machine learning models with minimal effort and time investment.

💡Custom Modeling

Custom Modeling refers to the process of building machine learning models that are tailored to specific business needs or complex use cases. It involves a higher level of effort and expertise compared to AutoML, as it requires the manual selection of model architecture, training methods, and fine-tuning of parameters. In the context of the video, Vertex AI provides a platform for custom model training, allowing startups with ML experts to build highly customizable models.

💡BigQuery ML

BigQuery ML is a feature of Google Cloud's BigQuery service that allows users to create machine learning models using simple SQL queries. It enables the streaming of data and the creation of descriptive or predictive models directly within the BigQuery data warehouse, even at petabyte scale. In the video, BigQuery ML is presented as an option for startups that want to create ML models without the need for extensive data preprocessing or customizability.

💡Data Types

Data Types refer to the classification of data based on its nature and the type of values it contains. In machine learning, understanding data types is crucial as different algorithms perform better with certain types of data. In the video, data types such as text, image, video, and tabular data are mentioned, each suitable for different machine learning tasks and models.

💡Model Training

Model Training is the process of teaching a machine learning model to make predictions or decisions based on historical data. It involves feeding data into the model so that it can learn patterns and relationships within the data. In the video, model training is a central theme, with an emphasis on how Google Cloud's services, like Vertex AI and BigQuery ML, facilitate and streamline this process for startups.

💡Model Deployment

Model Deployment refers to the process of putting a trained machine learning model into operation, where it can start making predictions on new data. This is a critical step in the machine learning workflow, as it transitions the model from development to real-world use. In the video, model deployment is discussed in the context of Vertex AI, highlighting how startups can deploy their models to endpoints for serving predictions.

Highlights

Google Cloud offers a variety of machine learning solutions designed to help startups build and grow their businesses successfully and sustainably.

The four broad categories of machine learning offerings on Google Cloud include ML APIs, Vertex AI's AutoML, custom modeling in Vertex AI, and BigQuery ML.

ML APIs are ideal for quick starts with minimal effort and no customizability, making them perfect for organizations looking to rapidly integrate machine learning into their workflows.

AutoML within Vertex AI is suitable for organizations needing more customization and willing to invest time and effort, catering to various data types such as text, images, video, and audio.

For teams with ML experts, Vertex AI's custom modeling provides a highly customizable platform for building models tailored to specific needs.

BigQuery ML (BQML) enables the creation of descriptive and predictive ML models using simple SQL queries, even with data stored in petabyte-scale data warehouses.

Vertex AI is a comprehensive suite of machine learning products and services that streamline the ML workflow from data readiness to model deployment and management.

AutoML simplifies the traditional ML workflow by pre-building models, processing data, and handling feature engineering, model selection, and hyperparameter tuning automatically.

Google's Model Zoo provides a vast array of pre-built models that AutoML uses to find the best fit for your data, streamlining the process from data to value.

Custom modeling in Vertex AI allows for the creation of bespoke models, offering flexibility and control for complex and niche use cases.

A graphical, codeless interface guides users through the ML lifecycle in Vertex AI, with automated steps and safeguards at each stage.

Vertex AI's unified platform supports reusing models, building pipelines, and creating automated workflows, all built on Google's robust and secure cloud infrastructure.

AutoML's advanced deep ensemble methods and model portfolio provide a wealth of options, cherry-picked from the best of research without the user needing to delve into the research themselves.

Custom training in Vertex AI offers the ability to choose from pre-built containers or bring custom containers, providing a tailored environment for model training.

Hyperparameter tuning can be performed within Vertex AI, optimizing model performance by adjusting parameters, data types, and scaling methods.

Deploying models in Vertex AI is straightforward, allowing for quick deployment to production with just a few clicks, significantly reducing the time from development to deployment.

Fertile Mdcina, a Brazilian digital healthcare startup, has leveraged Vertex AI and TensorFlow to enhance ML diagnostic reliability and reduce deployment cycles, showcasing the practical applications of Google Cloud's AI capabilities.

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