Professional Machine Learning Engineer

Scaling Online Model Serving

Managing and Serving Features Using Agent Platform Feature Store

The Agent Platform Feature Store is a managed service that stores and serves precomputed features for machine learning models in production. Features are the input variables that a model uses to make predictions, and the Feature Store ensures that the same features used during training are available at serving time. When a request comes in, the serving system retrieves the relevant features from the Feature Store and passes them to the model for prediction. This consistency between training and serving is critical because models can make incorrect predictions if the features at serving time differ from what they learned on during training. The Feature Store also handles feature versioning and allows teams to update feature values without retraining the model.

Deploying Models to Public and Private Endpoints

Model endpoints are the URLs where client applications send prediction requests, and choosing between public and private endpoints involves security and accessibility tradeoffs. A public endpoint is accessible from the internet and is suitable when external applications or users need to make predictions. A private endpoint can only be reached from within the Google Cloud network, which protects the model from unauthorized access. When deploying to a private endpoint, traffic flows from the client through Google's internal network to the model, never exposing the model directly to the public internet. Teams should choose private endpoints when the model contains sensitive data or when only internal services need to make predictions.

Choosing Appropriate Hardware

The type of hardware that runs a model during serving affects both prediction speed and cost. Central Processing Units (CPUs) are general-purpose processors that work well for simpler models or when latency requirements are not strict. Graphics Processing Units (GPUs) contain thousands of small processors that run many calculations in parallel, making them ideal for large deep learning models that need fast predictions. Tensor Processing Units (TPUs) are Google-designed chips optimized specifically for tensor operations common in machine learning, and they often provide the best performance for TensorFlow and JAX models. Edge devices are small, low-power computers that run models directly on devices like phones or IoT sensors, reducing latency by processing data locally instead of sending it to a server. The choice depends on the model complexity, the number of requests per second, and the acceptable latency.

Scaling the Serving Backend Based on Throughput

Throughput is the number of prediction requests that a system can handle in a given time period, and scaling the serving backend ensures the system meets demand. When request volume increases, the system adds more serving instances to handle the load; when volume decreases, it removes instances to save costs. Gemini Enterprise Agent Platform Inference and containerized serving are two approaches for handling high-throughput model serving. Containerized serving packages the model and its dependencies together, allowing it to run consistently across different environments and scale horizontally by adding more containers. The scaling process automatically distributes incoming requests across all available instances, so no single instance becomes overwhelmed. Health checks ensure that only healthy instances receive traffic, and scaling decisions are based on metrics like request queue length or CPU utilization.

Tuning ML Models for Training and Serving in Production

Model tuning adjusts the model architecture and parameters to balance accuracy, speed, and resource usage in production. During training, models learn patterns from data, but production environments often have different data distributions or stricter latency requirements. Techniques like quantization reduce the precision of model weights from 32-bit to 8-bit numbers, making the model smaller and faster while sacrificing some accuracy. Pruning removes unnecessary connections in the model, reducing its size and computational requirements. The serving stack must also be optimized, including choosing the right serialization format for model files and configuring batch processing to handle multiple requests efficiently. These tuning steps ensure the model performs well under real-world serving conditions.

Key Facts

• The Agent Platform Feature Store ensures consistency between training and serving by providing the same features to the model at prediction time.
• Private endpoints restrict model access to internal Google Cloud network traffic, while public endpoints allow internet-accessible predictions.
• GPUs excel at parallel processing for deep learning models, while TPUs are optimized for tensor operations in TensorFlow and JAX models.
• Containerized serving scales horizontally by adding more containers, distributing requests across healthy instances automatically.
• Quantization and pruning are tuning techniques that reduce model size and latency for production serving.

Frequently Asked Questions

What is the difference between a public endpoint and a private endpoint for model serving?

A public endpoint is accessible from anywhere on the internet, while a private endpoint can only be reached from within the Google Cloud network. Private endpoints provide better security for sensitive models.

When should I use GPUs versus TPUs for model serving?

Use GPUs for models that benefit from general parallel processing or when using frameworks other than TensorFlow. Use TPUs when running TensorFlow or JAX models, as TPUs are specifically designed for tensor operations and often provide better performance for these frameworks.