AutoML in Vertex AI: Understanding the Relationship

AutoML, or Automated Machine Learning, is a suite of tools within Google Cloud's Vertex AI that helps automate various aspects of the machine learning (ML) workflow. This includes everything from data preparation and feature engineering to model training, hyperparameter tuning, and even deployment.

Instead of manually performing these tasks, AutoML leverages algorithms and techniques to optimize the entire ML pipeline. In turn, this reduces the time and effort needed to build and deploy high-performing models.

Vertex AI, Google's fully managed unified platform, works synergistically with AutoML and empowers this process by offering a comprehensive and accessible platform for building and deploying ML models.

Let's dive into the relationship between these two systems and how your organization can leverage the capabilities of Vertex AI to take model building to the next level.

Defining AutoML & Vertex AI

As mentioned, AutoML and Vertex AI have a synergistic relationship. However, the two systems are very different. Here's how:

AutoML

• Automated model building: AutoML automates many aspects of model training, like feature engineering, hyperparameter tuning, and model selection. This makes it accessible to users with limited machine-learning expertise.
• Support for various data types: AutoML can handle different data types, like images, text, and tabular data, allowing you to build models for diverse tasks.
• Simplified workflow: AutoML integrates seamlessly with Vertex AI, streamlining the ML development process from data preparation to model deployment.

Vertex AI

• Managed infrastructure: Vertex AI provides a managed platform for running AutoML jobs, eliminating the need for setting up and managing your own infrastructure.
• Scalability and performance: Vertex AI offers scalable resources and high-performance computing options, enabling you to train complex models efficiently.
• Deployment and monitoring: Vertex AI facilitates deploying trained models to production and provides tools for monitoring their performance in real time.

Think of it like this...

• AutoML is the engine: It drives the automated model-building process, making it accessible and efficient.
• Vertex AI is the platform: It provides the infrastructure, resources, and tools to run AutoML effectively and manage the entire ML lifecycle.

As we'll see, AutoML and Vertex AI can work together in many ways. Here are some common use cases:

• Image classification: You can use AutoML Tables on Vertex AI to automatically train an image classifier for tasks like product recognition or anomaly detection.
• Text sentiment analysis: AutoML Natural Language on Vertex AI can train a sentiment analysis model to classify customer reviews or social media posts.
• Time series forecasting: AutoML Tabular on Vertex AI can automatically build a forecasting model to predict future sales or resource consumption.

Overall, AutoML and Vertex AI work in tandem to democratize machine learning and make it accessible to a wider range of users. They provide a powerful combination of automation, scalability, and comprehensive tools for building and deploying effective ML models.

How Does AutoML in Vertex AI Improve Developer Productivity?

AutoML improves developer productivity in a variety of ways:

• Reduces repetitive coding for data pre-processing and feature engineering.
• Eliminates tedious parameter tuning and repeated training runs.
• Enables quick prototyping and experimentation, accelerating model development.
• Frees up developer time from mundane tasks to focus on core modeling.
• Provides reusable templates and best practice blueprints for common ML tasks.
• Integrates seamlessly with developer workflows in notebooks and workflows.
• Generates documentation and allows model monitoring reducing maintenance.
• Enables collaboration allowing developers to build on top of each other's work.
• Reduces compute resource usage by automatically scaling down experiments.

The Importance of Using Vertex AI for AutoML Datasets & Models

In classical programming, the programmer needs to specify step-by-step instructions for a computer to follow. For example, if you wanted to build a model to reply to customer comments, the programmer would need to create rules that specify vocabulary and structure and provide instructions on how the model should respond to each.

Even in a simple example like this, the instructions quickly become confusing and overwhelming. It's nearly impossible for a programmer to come up with all the scenarios and anticipate potential responses.

This demonstrates the need for a system that can generalize a variety of comments (or other data types) and categorize them. Further, the system must be able to learn over time from examples and continuously improve. AutoML can do just that. But the capabilities don't stop there.

When you integrate Vertex AI into your AutoML process, you can harness the ability of supervised learning tasks to achieve the desired outcome. Vertex AI uses these supervised learning tasks to train, test, and validate the ML model you've created to refine its abilities and solve a range of different problems.

Vertex AI works with various model types, including:

• Image: You can train, test, and validate your ML model with example images annotated with labels for classification or labels and bounding boxes for object detection. Using supervised learning, you can train a model to recognize patterns and content you care about in images.
• Tabular: Vertex AI uses structured data (called tabular data) to train machine learning models that make predictions on new data. Imagine you have a dataset with various columns: one "target" column you want the model to predict and several "feature" columns that provide information the model learns from. You can use the same set of features to build different models simply by changing the target and training options! Think of it like this: you have a recipe and various ingredients. By changing the main ingredients and cooking methods, you can create different dishes from the same base.
• Text: Vertex AI allows you to perform supervised learning. This involves training a computer to recognize patterns from labeled data. So, you can train an AutoML model to recognize content in text.
• Video: You can train ML models with videos you've already labeled. With a trained model, you can input new videos into the model, which outputs video segments with labels. A video segment defines the start and end time offset within a video (this could be a whole video or a user-defined segment). A label is a predicted "answer" from the model.

Integrating Vertex AI into Your ML Workflow

The process of integrating Vertex AI into your ML workflow may vary depending on your project, but Vertex AI uses a standard workflow that follows these steps:

1. Gather Data: First, you must determine the data needed for training and testing your model based on the outcome you want to achieve.
2. Prepare Data: Next, ensure your data is properly formatted and labeled.
3. Train: Now, set parameters and build your model.
4. Evaluate: Review model metrics.
5. Deploy and Predict: Finally, make your model available to use.

Let's explore these steps in greater detail.

Data Preparation & Gathering

First, you must determine the outcome you want to achieve. Consider questions like:

• What do I want my model to do?
• What kinds of data (images, text, video, etc.) do I need the model to recognize to achieve this outcome?
• What examples best reflect the type and range of data I want my system to see and classify?

Once you've established what data you need, it's time to source it. You may have this data in-house or need to source it from a third-party provider. For example, if you wanted to train a model based on images, the bare minimum required by Vertex AI training is 100 image examples per category for classification.

But the more examples you have, the better. Target at least 1,000 examples per label. It's important to distribute examples equally across categories and try to ensure your data captures the variety and diversity of the problem you want to solve.

Finally, prepare your data by adding it to Vertex AI. For images, for example, you can import data from your computer or from Cloud Storage in an available format (CSV or JSON Lines) with labels inline. Learn more about preparing your training data here.

Training Model

Once your data is imported, you can train your model. Vertex AI generates a reliable ML model with training defaults, but you can adjust parameters based on your needs. Your dataset will be split into training, validation, and testing sets. The split Vertex AI applies depends on the model type you are training. Learn more about data splits for AutoML models here.

Because Vertex AI works with images, tabular models, text, and video, the training process looks different for each. As mentioned, you can adjust the parameters set by Vertex AI to fit your use case.

Evaluating Model

After training, you'll receive a summary of your model's performance. Model evaluation metrics are based on how the model performed against a slice of your dataset.

In a tabular model, for example, you'll review metrics like a "confidence score," which is the number that determines the model's certainty that the predicted class is correct. Another metric for tabular models is precision and recall, which help you understand how well your model is capturing information and what it's leaving out.

Keep in mind that evaluation metrics differ from image, tabular, text, and video models. However, Vertex AI provides a summary for each so you can see how well your model correctly predicts (or incorrectly) based on the data you gave it.

Testing Model

Depending on the type of model you have, you can test it in various ways for improvement.

For example, for tabular data, evaluating your model metrics is the best way to determine whether your model is ready to deploy, though you can test it with new data.

On the other hand, for an image model, Vertex AI uses 10% of your data automatically to test the model, and then the "Evaluate" page tells you how well the model did with that data.

Deploying Model

Once you're satisfied with your model's performance, you can deploy and use it. In some cases, this may be in a batch prediction mode which is useful for making many prediction requests at once.

Online prediction, however, only accepts one prediction request per API call. Online prediction is useful if your model is part of an application and your system needs a quick prediction turnaround.

When you're finished using your model, you can delete the resources you created to avoid incurring charges to your account. Learn more about cleaning up projects:

• Clean Up My Image Data
• Clean Up My Tabular Data
• Clean Up My Text Data
• Clean Up My Video Data

The Benefits of Using Vertex AI with AutoML

Although each ML workflow is unique, it's undeniable that combining Vertex AI with AutoML streamlines efficiency like never before. Promevo is here to help you start the process of harnessing these tools for the betterment of your organization. Key benefits of using AutoML in Vertex AI include:

Democratizing Machine Learning

• AutoML removes the need for extensive ML expertise. Its automated approach allows even beginners to build and train models effectively.
• Vertex AI provides a user-friendly platform. The intuitive interface simplifies the entire ML workflow, from data preparation to deployment.
• You'll benefit from faster experimentation and iteration. AutoML's automated tuning and hyperparameter selection enable quicker testing and refinement of models.

Improved Efficiency & Performance

• Automated tasks reduce development time and resources. AutoML handles repetitive tasks like feature engineering and hyperparameter tuning, freeing up data scientists for higher-level tasks.
• Vertex AI's managed infrastructure and scalability streamline resource allocation and enable the handling of large datasets and complex models efficiently.
• AutoML often finds better-performing models. Its ability to explore a wider range of options compared to manual methods can lead to superior model accuracy and prediction power.

Enhanced Flexibility & Control

• Support for various data types: Vertex AI and AutoML can handle diverse data, including images, text, tabular data, and time series.
• Build multiple models with the same data: You can leverage the same feature set to train different models by changing the target and training options, offering versatility for various tasks.
• Customization options: While AutoML automates many aspects, you still have control over model selection, training parameters, and deployment configurations.

Partner with Promevo Today

As Google Cloud's unified machine learning platform, Vertex AI aims to make your path to digitally transforming with AI technology faster and more effective. If you're ready to incorporate Vertex AI into your workflow, Promevo is here to help.

As a certified Google partner, Promevo can guide you step by step on your Vertex AI journey. Our team has deep expertise in all things Google. We stay on top of product innovations and roadmaps to ensure our clients deploy the latest solutions to drive competitive differentiation with AI.

Through our comprehensive services spanning advisory, implementation, and managed services, you get a true partner invested in realizing your return outcomes — not just delivering tactical tasks. Our solutions help connect workflows across your stack to accelerate insight velocity flowing from Vertex AI models put into production.

Contact us to discover why leading enterprises trust Promevo to maximize their Vertex AI advantage day in and day out. Together, we will strategize high-impact AI opportunities customized to your goals.

FAQs: AutoML in Vertex AI

Is AutoML part of Vertex AI?

AutoML is not a separate product, but rather a collection of functionalities within Vertex AI. Think of it as a powerful toolset integrated into the Vertex AI platform, specifically focused on automating various aspects of the machine learning workflow.

Here's a breakdown of the relationship:

• Vertex AI is the overarching platform: It provides a unified environment for managing the entire ML lifecycle, from data preparation to model deployment and monitoring.
• AutoML sits within Vertex AI: It offers specific features within Vertex AI, focusing on automated tasks like feature engineering, model selection, and hyperparameter tuning.

Do data scientists use AutoML?

Yes, data scientists do use AutoML, but its role in their workflow depends on their experience and project goals.

Here's a breakdown:

For experienced data scientists:

• AutoML automates repetitive tasks like feature engineering and hyperparameter tuning, freeing up time for higher-level analysis and model interpretation.
• AutoML can quickly test multiple model architectures and configurations, providing valuable insights and benchmarks for further refinement.
• AutoML can sometimes find unexpected patterns and features that manual methods might miss, sparking new ideas for model design.

For less experienced data scientists:

• AutoML lowers the barrier to entry in ML, allowing them to build and deploy models without needing deep technical expertise.
• AutoML provides a hands-on learning experience, helping them understand model training processes and the impact of different parameters.
• AutoML can quickly generate initial models, allowing them to focus on data preprocessing and problem definition before diving into complex model architectures.