What is an AI workload?

AI and ML workloads can be broadly categorized in two ways

• Stage in the AI life cycle they represent (like data preprocessing, training, and inference)
• Type of task they perform (like natural language processing or computer vision)

Understanding these types helps technical decision-makers plan for the specific infrastructure, compute power, and orchestration strategies each one demands.

AI workloads are at the heart of digital transformation, delivering high-impact, real-world applications across nearly every industry, turning data into practical value.

AI workloads can power recommendation engines for retail, e-commerce, and media companies. For example, a streaming company uses a sophisticated ML model, trained on billions of viewing habits, to provide highly personalized content suggestions.

Manufacturers deploy sensors on critical equipment, generating massive time-series data. AI workloads can continuously analyze this data to predict mechanical failures days or weeks in advance, allowing for scheduled maintenance.

Financial institutions use machine learning workloads to analyze millions of transactions in real-time. These models can identify patterns indicative of fraud, with some systems detecting unauthorized transactions with a high degree of accuracy and low false-positive rate.

Computer vision workloads are used to analyze medical images like X-rays, CT scans, and MRIs. These AI models can flag potential anomalies, such as early-stage tumors, often with speed and consistency that aids human clinicians in making faster and more accurate diagnoses.

Workloads based on generative AI models are helping transform creative and technical fields. They're used to automatically generate marketing copy, synthesize realistic images for advertising, create virtual meeting summaries, or even assist developers by suggesting and completing code blocks.

Deploying a trained machine learning model for inference can be a key step in productionizing an AI workload. Vertex AI simplifies this process by providing managed services that handle the underlying infrastructure.

Upload the trained model to the model registry

• The first step is to take your trained model artifact and upload it to the Vertex AI Model Registry. This central repository securely stores and versions your models, making them ready for deployment.

Create a managed endpoint

• Next, you create an endpoint, which is a dedicated, real-time HTTP server for your model. This endpoint is the URL that your applications will call to get predictions. You define the type of compute resources it will use, such as an N1 CPU machine or a specific type of GPU for accelerated performance.

Deploy the model to the endpoint

• After creating the endpoint, you deploy a specific version of your model to it. This step involves specifying the container image that includes your model and the prediction server code (often a pre-built image provided by Vertex AI). You also configure traffic splits, which allow you to test a new model version with a small percentage of live traffic before rolling it out completely.

Send and receive online predictions

• Once deployed, the model is available for online prediction. Your application sends input data (the payload) via an HTTP request to the endpoint's URL, and the managed service handles the inference workload, returning the prediction or result in near real-time.

Monitor and govern the endpoint

• The final step is continuous monitoring. You use Vertex AI's integrated tools to track the health of the endpoint (latency, error rates, resource utilization) and the performance of the model itself (drift, skew, and prediction quality) to ensure the inference workload remains reliable and accurate over time.