If you’re considering using Vertex AI to train and deploy your models, you’re on the right track! Data is essential for machine learning, and the more data a model has and the higher quality it is, the better the model will perform. Before training a model, the data must be preprocessed, which means cleaning, transforming, and aggregating it into a format that the model can understand. Data preprocessing is also important when serving a model, but it can be more complex due to factors such as real-time streaming data, hardware scalability, and incomplete data.
When you’re handling large amounts of data, you need a service that’s both scalable and reliable. Dataflow fits the bill perfectly, as it can process data in both real-time and batch mode, and it’s ideal for models with high throughput and low latency requirements.
Dataflow and Vertex AI work great together, so keep reading to learn how to use these two powerful services to serve models for streaming prediction requests.
Use Case: Streaming Prediction Requests
Certain applications, such as anomaly detection in sensor data and predictive maintenance for industrial equipment, demand real-time predictions from machine learning models. Surprisingly, implementing real-time prediction systems doesn’t require an overly complex setup. If your machine learning model needs to make predictions on real-time data, a straightforward approach involves utilizing a Pub/Sub topic to capture real-time data, a Dataflow pipeline to preprocess and transform the data, and a Vertex AI endpoint to execute the machine learning model and generate predictions. Additionally, you can enable model monitoring to track any data or model changes that could impact prediction accuracy. The following diagram illustrates the workflow of this solution:
