Google Vertex AI consultants

Q: What is the difference between Vertex AI and Google Cloud AI APIs?

Google Cloud AI APIs are pre-built, ready-to-use services for specific tasks like translation, speech recognition, or vision. Vertex AI is the broader ML platform that includes these APIs but also provides tools for building, training, and deploying custom models. Use the APIs for standard tasks; use Vertex AI when you need custom models or end-to-end ML workflows.

Q: Do I need machine learning expertise to use Vertex AI?

It depends on your use case. AutoML features let you train custom models with minimal ML knowledge — you provide labelled data and Vertex AI handles the model architecture and training. For pre-trained models and APIs, you just need developer skills. Custom training with frameworks like TensorFlow or PyTorch requires ML expertise.

Q: How does Vertex AI pricing work?

Vertex AI uses consumption-based pricing across multiple dimensions: compute resources for training, prediction serving, storage, and specific feature usage. Costs vary significantly depending on model size, training duration, and prediction volume. Google provides a pricing calculator, but real costs are difficult to estimate without running initial experiments.

Q: Can Vertex AI work with data stored outside Google Cloud?

Yes, though it works most seamlessly with data in Google Cloud services like BigQuery and Cloud Storage. You can import data from external sources, but there will be data transfer time and potentially costs involved. For ongoing workflows, keeping your ML data within Google Cloud reduces latency and complexity.

Q: What is AutoML in Vertex AI?

AutoML automates the process of selecting the right model architecture, tuning hyperparameters, and training a model on your data. You provide labelled training data and specify the task type (classification, object detection, text analysis, etc.), and AutoML produces a trained model. It makes custom model creation accessible to teams without deep ML expertise.

Q: How does Vertex AI handle model monitoring in production?

Vertex AI includes model monitoring features that track prediction quality over time, detect data drift (when incoming data starts differing from training data), and alert you to potential model degradation. This is critical for production deployments where model accuracy can decline as real-world data patterns change.

We can help you automate your business with Google Vertex AI and hundreds of other systems to improve efficiency and productivity. Get in touch if you’d like to discuss implementing Google Vertex AI.

Get in touch

Book a call

About Google Vertex AI

Google Vertex AI is Google Cloud’s unified machine learning platform, bringing together Google’s AI capabilities — from pre-trained models and AutoML to custom model training — under a single managed environment. It’s built for organisations that need production-grade AI and ML capabilities without managing the underlying infrastructure from scratch.

The platform covers the full ML lifecycle: data preparation, model training, evaluation, deployment, and monitoring. You can use pre-trained models for common tasks like natural language processing, image recognition, and speech-to-text, or train custom models on your own data using AutoML (which automates much of the model selection and tuning process). For teams with ML expertise, Vertex AI also supports custom training with frameworks like TensorFlow, PyTorch, and scikit-learn.

Where Vertex AI differentiates itself is in the enterprise infrastructure layer. It handles model versioning, A/B testing, prediction serving at scale, model monitoring for drift, and integration with the broader Google Cloud ecosystem — BigQuery, Cloud Storage, Dataflow, and more. For organisations already running workloads on Google Cloud, Vertex AI fits naturally into existing data pipelines. Businesses working on automated data processing at scale often find that Vertex AI provides the ML backbone needed to extract intelligence from their data.

Vertex AI is a powerful platform, but it’s also complex. Getting real value from it requires clear problem definition, good data, and ML expertise — whether in-house or through partners. Working with experienced AI consultants helps organisations avoid common pitfalls like training models on poor data or deploying solutions that don’t align with business objectives. For companies building sophisticated AI capabilities, Vertex AI paired with custom AI development services provides an enterprise-grade foundation.

Google Vertex AI FAQs

Frequently Asked Questions

Common questions about how Google Vertex AI consultants can help with integration and implementation

What is the difference between Vertex AI and Google Cloud AI APIs?

Do I need machine learning expertise to use Vertex AI?

How does Vertex AI pricing work?

Can Vertex AI work with data stored outside Google Cloud?

What is AutoML in Vertex AI?

How does Vertex AI handle model monitoring in production?

How it works

We work hand-in-hand with you to implement Google Vertex AI

As Google Vertex AI consultants we work with you hand in hand build more efficient and effective operations. Here’s how we will work with you to automate your business and integrate Google Vertex AI with integrate and automate 800+ tools.

Step 1

Define Your ML Problem Clearly

Before touching the platform, define exactly what you’re trying to predict or classify, what data you have, and what business decision the model will inform. A vague problem definition leads to wasted time and compute resources. Be specific about inputs, outputs, and success criteria.

Step 2

Prepare and Upload Your Data

Gather, clean, and label your training data. Upload it to Google Cloud Storage or BigQuery. Data quality is the single biggest factor in model performance — spend more time here than you think necessary. Vertex AI includes data labelling tools if your data isn’t already labelled.

Step 3

Choose Your Training Approach

Decide between pre-trained models (fastest, least customisation), AutoML (moderate effort, good customisation), or custom training (most effort, maximum control). The right choice depends on your data, the complexity of your problem, and the ML expertise available to your team.

Step 4

Train and Evaluate Your Model

Run your training job and evaluate the results using Vertex AI’s evaluation metrics. Look at accuracy, precision, recall, and other relevant metrics for your problem type. If performance isn’t adequate, iterate on your data quality, feature selection, or training approach.

Step 5

Deploy to a Prediction Endpoint

Deploy your trained model to a Vertex AI endpoint for serving predictions. Configure the compute resources based on your expected prediction volume and latency requirements. Set up A/B testing if you’re comparing model versions.

Step 6

Monitor and Maintain in Production

Enable model monitoring to track prediction quality and data drift over time. Set up alerts for significant performance changes. Plan for periodic model retraining as new data becomes available and real-world patterns shift. ML models are not set-and-forget — ongoing maintenance is essential.

Transform your business with Google Vertex AI

Unlock hidden efficiencies, reduce errors, and position your business for scalable growth. Contact us to arrange a no-obligation Google Vertex AI consultation.

Get in touch

Book a call