Vertex AI is a machine learning (ML) platform designed for enterprises to develop, train, and deploy custom AI models. Built by Google Cloud, Vertex AI enables companies to integrate artificial intelligence (AI) applications into their business workflows, providing access to advanced tools that combine data science, ML engineering, and data processing.
At its core, it’s a platform that makes it easy for technology and business teams to efficiently collaborate on AI projects and scale models with Google Cloud infrastructure.
Training and deploying models in Vertex AI
Vertex AI offers multiple options for model development and training, ranging from no-code approaches to complex customizations, tailored to the needs and skills of each team:
AutoML: Allows companies to train models on tabular data, images, text, and video without writing code. This is ideal for those looking for speed in development without the need for deep technical knowledge.
Custom Training: Gives data teams full control over the model training process. They can use their own frameworks, tune hyperparameters, and customize training code to their specific needs.
Model Garden: Facilitates the search, customization, and testing of open source (OSS) models and components, providing flexibility and time savings when building AI models.
Generative AI: Vertex AI also allows you to work with large generative models from Google, such as Large Language Models (LLM), to build advanced AI solutions on text, images, speech, and code. These models can be customized to business needs.
MLOps in Vertex AI: Automation and Scalability
Once models are trained and deployed, Vertex AI offers MLOps (machine learning operations) tools that allow projects to be automated and scaled throughout their lifecycle. The infrastructure is fully managed, freeing companies from managing complex technical aspects, allowing them to focus on maximizing the value of their models.
Notable MLOps features include:
- Version control and monitoring: Vertex AI makes it easy to register models in the Model Registry, where versions are managed and transfer to production is enabled.
- Continuous monitoring: The platform monitors the performance of models in production, detects biases or changes in data, and provides alerts so that models can be adjusted or retrained if necessary.
Vertex AI Workbench: Collaboration in data teams
Vertex AI Workbench is a Jupyter Notebooks-based development environment that allows data science teams to easily collaborate on AI projects. Its integration with other Google Cloud tools, such as Cloud Storage and BigQuery, makes it easy to access and process large volumes of data.
Using Colab Enterprise allows teams to develop models in a shared and scalable environment. This collaboration is key to optimizing workflows, especially when managing complex projects with multiple specialists working in parallel.
Vertex AI Workflow: From Data Preparation to Deployment
The machine learning workflow in Vertex AI follows a structured process that includes the following stages:
1. Data Preparation
Before training a model, it is essential to clean and prepare the data. Vertex AI facilitates this stage through its notebooks, allowing teams to perform exploratory data analysis (EDA) to identify relevant patterns, attributes, and variables.
In addition, the platform offers integration with BigQuery and Cloud Storage to process large volumes of data quickly. If the data is extremely large, workloads can be run on Spark serverless through Dataproc, optimizing the handling of these complex data sets.
2. Model training
At this stage, companies can opt for AutoML or train their own custom models. AutoML is ideal for teams looking for efficiency without writing code, while custom models allow for more precise control of the process.
For teams requiring more advanced tuning, Vertex AI has tools like Vertex AI Vizier, which automatically optimizes hyperparameters. Additionally, Vertex AI Experiments makes it easy to compare different experiments to choose the most efficient approach.
3. Evaluation and tuning
Once the model is trained, its performance is evaluated using metrics such as precision and recall. If the model does not achieve the expected results, adjustments can be made to both the data and hyperparameters, allowing for more efficient iterations.
The Vertex AI Model Registry makes it easy to integrate continuous evaluations into workflows, ensuring that models are constantly compared and tuned.
4. Model deployment
Once the model is ready, it can be deployed into production to obtain real-time or batch predictions. Vertex AI offers several deployment options, including predictions through pre-configured or custom containers.
Additionally, for models trained with BigQuery ML, predictions can be obtained directly from BigQuery, streamlining the integration of AI into business processes that rely on big data analytics.
5. Model Monitoring
After deployment, it is critical to monitor model performance. Vertex AI has Model Monitoring tools, which continuously monitor models in production and detect drift or bias in the data.
If the model starts to degrade, either due to changes in input data or usage patterns, the platform can send alerts for the data team to take corrective actions, such as retraining the model with new data.
Vertex AI as a business innovation engine
Vertex AI offers a comprehensive solution for companies that want to harness the power of machine learning and artificial intelligence in their operations. From data preparation to model deployment and monitoring, Vertex AI provides the tools necessary for data and technology teams to work together efficiently.
Companies that implement Vertex AI can scale their AI solutions and take advantage of Google Cloud's robust infrastructure, obtaining more accurate and efficient models that drive innovation and optimize internal processes. With Vertex AI, any company can accelerate its digital transformation and stay competitive in an increasingly AI-driven world.
