Optimizing MLOps on Vertex AI: Streamline Your ML Workflow with Google

Machine learning operations (MLOps) streamline the deployment of models into production and the management of updates, but they can be complex to implement. Google Cloud’s Vertex AI simplifies MLOps by providing an integrated platform to automate, monitor, and optimize the entire machine learning lifecycle.

With Vertex AI, teams can quickly transition models from proof-of-concept to full production systems while lowering costs and minimizing errors.

Vertex AI Overview

Google's Vertex AI is a cloud-based machine learning platform that makes it easier for teams to build and deploy artificial intelligence apps and services. Vertex AI provides a simplified platform for the entire machine learning process, allowing less technical teams to leverage AI while giving experts advanced capabilities.

Key capabilities and benefits include:

• Automated ML (AutoML): Automatically builds machine learning models from your data without requiring you to code
• Custom Model Building: Offers full control for experts to write code and customize model architectures
• MLOps: Automates and optimizes deploying models into applications and managing updates
• GPU Acceleration: Speeds up model training by leveraging powerful GPUs in the cloud
• Pricing: Only pay for what you use with per-second billing and auto-scaling

In simple terms, Vertex AI handles a lot of the heavy lifting involved with turning data into usable AI applications. Some of the main workflows it supports are:

• Data ingestion and preparation
• Training machine learning models with AutoML or writing your own code
• Evaluating models to compare and select the best performers
• Deploying models into applications and APIs
• Monitoring models and retraining them as data changes over time

The goal with Vertex AI is to simplify the process so that less technical users can benefit from AI while still providing advanced functionality for data scientists. It turns Google’s latest AI research into easy-to-use services for building real-world solutions.

What Are MLOps?

Machine Learning Operations (MLOps) refers to the practices and systems for deploying machine learning models into production and managing updates to them over time. The goal of MLOps is to make ML systems more reliable, efficient, and accurate.

Vertex AI includes a set of integrated tools to implement MLOps:

• Pipelines: Automates the steps for preparing data, training models, evaluating performance, and deployment. Allows you to track these workflows.
• Metadata: Records all the detailed parameters, code versions, metrics, etc., used in model training and deployment. This allows tracking experiments.
• Experiments: Compares performance of different machine learning models side-by-side by keeping track of the code, data, and results. Helps identify the best model.
• Model Registry: Central repository for storing trained models, with version control and metadata. Allows discovering, deploying, and managing models.
• Vertex AI Feature Store: Shared storage for the features/data used to train machine learning models. Enables reusing features across teams.
• Model Monitoring: Monitors models after deployment to detect "data drift," where the real-world data differs from the original training data. Alerts on drops in performance.

Vertex AI handles a lot of the complexity around deploying and managing machine learning systems in production. Its tools work together to automate flows, track experiments, deploy updates, monitor for issues, and more. This makes building AI applications faster, easier, and more reliable.

ML Features with Vertex AI

Machine learning models require relevant, high-quality data to train on. Preparing this data includes a process called feature engineering, which transforms raw data into measurable attributes that can be fed into models. These “features” need to be carefully tracked, stored, and served so they remain useful over time.

Vertex AI provides dedicated tools for managing machine learning features through all stages of the model development lifecycle.

Feature Stores

Feature stores are centralized repositories for storing, organizing, tracking, and serving the machine learning features used to train AI models. Vertex AI has two centralized feature store options:

Vertex AI Feature Store:

• Integrates feature storage into BigQuery for easier management
• Acts as a service layer to track metadata and serve features
• Supports vector embeddings and similarity searches
• Optimized for low-latency online serving

Vertex AI Feature Store (Legacy):

• Fully managed store inside Vertex AI
• Batch import features from data sources
• Apply access controls on features
• Embeddable online serving

The main difference is the newer store leverages BigQuery, while the legacy version contains everything within Vertex AI.

Benefits of Centralized Features

Having a centralized feature store enables:

• Feature Discovery: Easily find existing features to reuse.
• Consistent Serving: Features served from a single source of truth.
• Access Control: Manage permissions for feature access.
• Historical Data: Store both live and historical feature data.
• Embeddings: Vector representations of categorical data.

By centralizing features, teams can share and discover feature data much more easily. This accelerates model development by avoiding redundant feature engineering. It also improves consistency and governance for feature usage.

Vertex AI Model Registry

The Model Registry is a central repository within Vertex AI for organizing, tracking, and managing machine learning models. It provides an overview of all models in one place to streamline model lifecycle management.

Key capabilities include:

• Import and register models from various sources, including AutoML, custom training, and BigQuery ML.
• Assign version numbers to model iterations.
• Add labels, aliases, and other metadata to models.
• Directly deploy registered models to online prediction endpoints.
• Initiate batch predictions and model evaluations.
• View model details like metrics, parameters, artifacts, etc.

Having a registry makes it easier to:

• Keep track of model versions as they get retrained.
• Find and reuse the best-performing models.
• Promote models from experiments to production deployment.
• Govern and control access to model artifacts.
• Discover models across regions and projects using Dataplex catalog.

The Model Registry helps produce models more efficiently by establishing a single organized platform for model lineage, discovery, and lifecycle management after training. Teams can standardize and streamline the process of deploying and managing AI models in one place.

Look to Promevo for Help with Vertex AI

If you're looking to optimize your machine learning operations (MLOps) using Google Vertex AI, Promevo can help. As a Google Cloud Partner specializing in Vertex AI, we assist teams in implementing robust MLOps from edge to cloud.

Whether you need help setting up CI/CD pipelines, monitoring models, or migrating existing systems, Promevo has the hands-on Vertex AI experience to guide your success. We can help you:

• Automate machine learning workflows with Vertex AI Pipelines.
• Track experiments and model lineage with Vertex Metadata.
• Serve features faster with the Vertex AI Feature Store.
• Continuously retrain models to stay accurate over time.

As a certified Google partner, Promevo is focused exclusively on helping companies adopt Vertex AI to innovate faster. Contact our experts to discover how we can help you streamline your operations.