Efficient Workflows in Vertex AI: Simplify AI Development

Machine learning operations (MLOps) refers to the process of applying DevOps strategies to machine learning (ML) systems. Using DevOps strategies, you can build and release code changes and monitor systems to ensure you meet reliability goals.

When you use MLOps as part of this process, you can reduce the time it takes to go from data ingestion to deploying your model while also monitoring and understanding your ML system.

Using Vertex AI Pipelines, an innovative Google Cloud tool, you can automate, monitor, and govern your ML systems by orchestrating your ML workflow in a serverless manner, meaning you can store your workflow's artifacts using Vertex ML Metadata. This allows you to analyze the lineage of your workflow's artifacts, meaning you can easily track training data, code, and more that was used to create the model.

In essence, Vertex AI Pipelines help you streamline and automate your ML workflow in a way that's secure and scalable. Let's dive into everything you need to know about managing your workflows using Vertex AI.

Adopting AI for MLOps

Traditionally, it's been difficult for enterprises to adopt AI at scale and use it for ML workflows for various reasons, including:

• High degree of manual and one-off work.
• Poor handoffs between data scientists and IT.
• Models break when they fail to adapt to changes in the environment.
• Models don't make it into production.
• Lack of reusable or reproducible components.

Google is committed to educating and enabling MLOps best practices through systems like Vertex AI. Vertex AI is designed to build and train models five times faster than traditional notebooks while requiring fewer lines of code to build custom models.

Whereas other AI models have created hurdles for organizations that make full adoption difficult, Vertex AI is designed to relieve these issues through MLOps that allow users to manage, monitor, govern, and explain.

What Are Workflows in Vertex AI?

In Vertex AI, workflows are essentially automated pipelines that manage the entire ML lifecycle, from data ingestion and preparation to model training, evaluation, and deployment. They allow you to orchestrate and manage your ML tasks in a structured and efficient way, facilitating collaboration and scaling your AI applications.

Key types of workflows in Vertex AI include:

• Tabular Workflows: Specifically designed for tabular data, they automate tasks like feature engineering, model architecture search, training, and deployment. They offer end-to-end AutoML capabilities for tasks like classification, regression, and forecasting.
• Custom Workflows: Built using Vertex AI Pipelines, they provide flexibility for handling diverse data types and tasks beyond tabular data. You can define custom steps for data preprocessing, model training, evaluation, and deployment using various tools and components.

What Role Do Vertex AI Pipelines Play in Workflows?

To orchestrate your ML workflow using Vertex AI Pipelines, you first have to describe your workflow as a pipeline. ML pipelines provide the framework for defining and organizing your ML workflow as a series of connected steps. These steps may involve data preprocessing, model training, evaluation, and deployment. ML pipelines are composed of a set of input parameters and a list of steps, and each step is an instance of a pipeline component.

Using ML pipelines, you can:

• Apply MLOps strategies to automate and monitor repeatable processes.
• Run an ML workflow with different sets of parameters, training steps, or iterations for experimentation.
• Reuse a pipeline's workflow to train a new model, saving time and effort.

You can even use Vertex AI Pipelines to run pipelines that were built using the Kubeflow Pipelines SDK or TensorFlow Extended. You can learn more about choosing between the Kubeflow Pipelines SDK and TFX here.

An Explanation of Pipeline Components

Pipeline components are essentially self-contained sets of code that perform one part of a pipeline's workflow (like training a model or data transformation). These components are made up of a set of inputs, a set of outputs, and the location of a container image. A component's container image is a package that includes the components' code and a definition of the environment the code runs in.

Your team can build custom components or use prebuilt ones. If you want to use features of Vertex AI, like AutoML, in your pipeline, you can use the Google Cloud pipeline components to do so.

What Are Google Cloud Pipeline Components?

The Google Cloud Pipeline Components (GCPC) SDK provides prebuilt Kubeflow Pipelines components that are quality, performant, and easy to use. You can use GCPC to define and run ML pipelines in Vertex AI Pipelines and other ML pipeline backends that are conformant with Kubeflow Pipelines. You can tackle common tasks with ease like:

• Data Management: Create datasets, ingest diverse data types (images, tables, text, videos), and export data to Cloud Storage.
• Model Training: Leverage AutoML for image, tabular, text, or video data or run custom training jobs with containers or Python packages.
• Model Deployment: Upload existing models, create endpoints, and deploy models for online predictions.

GCPC are supported in Vertex AI Pipelines and offer many benefits, including:

• Standardized Artifact Types: You can provide consistent interfaces to use standard artifact types for input and output. These artifacts are tracked in Vertex ML Metadata which makes it easier for you to analyze artifact lineage.
• Easy Debugging: You can show the underlying resources launched from the component which allows for simple debugging.
• Understand Pipeline Costs: Resource labels are automatically propagated to Google Cloud services that are generated by the GCPC in your pipeline. You can use billing labels (along with Cloud Billing export to BigQuery) to easily review the cost of your pipeline run.
• Cost Efficiency: Vertex AI Pipelines optimize the use of GCPC by launching Google Cloud resources without having to launch the container. This reduces the startup latency and the cost of the busy-waiting container.

A Breakdown of Pipeline Workflow Using Vertex AI

Now that we've reviewed the relationship between workflow and the Vertex AI model pipelines let's explore how the process works.

Each step in a pipeline performs part of the pipeline's workflow, so each step has its own inputs, outputs, and a container image. Step inputs can be set from the pipeline's inputs or can depend on the output of other steps. These dependencies define the pipeline's workflow.

Here's an example of a pipeline and steps:

• Ingest Data: This step refers to loading training data into the pipeline. 
• Preprocess Data: Then, this step preprocesses the ingested training data.
• Train Model: This step uses the preprocessed data to train a model.
• Evaluate Model: In this step, the trained model is evaluated.
• Deploy: Now the trained model is deployed for predictions.

When you compile your pipeline, SDK (the Kubeflow Pipelines SDK or TFX) analyzes the data dependencies between steps to create the workflow graph. In this case:

• The ingest data step does not depend on any other steps or tasks. It can be the first step in your workflow or run concurrently with other steps.
• The preprocessing step relies on the first step (ingest data), so it must occur after ingesting data.
• The model training step relies on the preprocessing step, so training a model has to happen after preprocessing data.
• The model evaluation and deploying steps both rely on the training model step and must occur after the model is trained. Model evaluation and deployment can occur at the same time since they both depend on the model training step.

In this instance, the Vertex AI Pipelines run the ingest data, preprocess data, and model training steps in sequence. Then, it would run the model evaluation and deployment steps at the same time.

This is just one example of how Vertex AI Pipelines can help automate and simplify ML workflows. You can learn more about how to build a pipeline and how to run a pipeline, and you can consult the experts at Promevo for more information on integrating Vertex AI into your workflows.

The Importance of ML Artifacts

One of the key benefits of using Vertex AI Pipelines is that you can store your workflow's artifacts using Vertex ML Metadata. If you want to understand changes in the performance or accuracy of your ML system, you must be able to analyze this information and the lineage of ML artifacts.

An artifact's lineage includes all factors related to its creation and artifacts and metadata that were derived from this artifact. This is a lot of information, and managing this metadata in an ad-hoc manner is tough and time-consuming. A model's lineage could include information like:

• The training, test, and evaluation data used to create the model.
• Hyperparameters used during model training.
• Code used to train the model.
• Metadata recorded from training and evaluation.
• Artifacts that descend from this model.

Thankfully, using Vertex AI Pipelines, the artifacts and metadata are stored using Vertex ML Metadata. You can use this metadata to answer questions like:

• Which pipeline produced the most accurate model? What hyperparameters were used to train the model?
• Why did a specific pipeline run produce an especially accurate model?

You may also be able to use Vertex ML Metadata to answer system governance questions, such as determining which version of your model was in production at a certain point in time. This data is invaluable for developers who want to track project success and pinpoint areas for improvement without the stress of managing massive amounts of data.

Best Practices for Implementing Custom-Trained ML Models on Vertex AI

If you want to harness the capabilities of Vertex AI Pipelines, which are a fully managed service allowing you to retrain models as needed, it's important to follow best practices.

While retraining models allows you to adapt to changes and maintain performance, you should consider how much your data will change when choosing a model retraining cadence.

ML orchestration workflows are best for customers who have already designed and built a model, put it into production, and want to determine what is (or isn't) working. The code used for experimentation will likely be useful for the rest of the ML workflow, so ensure you have it available. Working with automated ML workflows does require you to be fluent in Python, understand basic container infrastructure, and have ML knowledge.

With these skills available, it saves you valuable time and effort to use Vertex AI Pipelines to orchestrate ML workflow as opposed to manually starting each data process, training, evaluation, testing, and deployment. Vertex AI Pipelines supports running DAGs generated by Kubeflow, TensorFlow Extended, and Airflow.

However, Kubeflow Pipelines SDK is recommended for most users who wish to author managed pipelines. Kubeflow Pipelines is flexible and provides Google Cloud Pipeline Components that let you include Vertex AI functionality like AutoML into your pipeline.

By following these best practices, you can use the robust capabilities of Vertex AI Pipelines to their full potential to streamline workflows, reduce tedious work, and create and deploy models tailored to your organization's needs.

Look to Promevo for Google Support

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 looking to leverage this technology for the betterment of your organization, Promevo can help.

Promevo is a certified Google partner with deep expertise in all things Google. Our team of experts can guide you step by step on your Vertex AI journey, ensuring you have the support needed to use the tool to its full potential. 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 disparate workflows across your stack to accelerate insight velocity flowing from Vertex AI models put into production. We care deeply about your success.

Contact us to discover why leading enterprises trust Promevo to maximize their Vertex AI investment. Together, we will strategize high-impact AI opportunities customized to your business goals.

FAQs: Workflows in Vertex AI

What is a workflow in machine learning?

A machine learning workflow defines which phases are implemented during a machine learning project. This may include data preprocessing, model training, model refinement, evaluation, deployment, and more.

What is a pipeline in Vertex AI?

In Vertex AI, a pipeline is a structured, automated workflow that manages the entire machine learning (ML) lifecycle. It essentially takes your raw data and guides it through various stages, from ingestion and preparation to model training, evaluation, and deployment. Think of it as an assembly line for your ML projects, ensuring each step happens in the right order and maximizing efficiency.