From Prototype to Prediction: Machine Learning on Google Cloud Vertex AI
Machine learning (ML) projects often move from experimentation to production, a process that involves various steps and tools. Google Cloud’s Vertex AI platform simplifies this transition by providing a comprehensive solution for developing, training, and deploying machine learning models. Here’s a breakdown of the process, from creating prototypes to making predictions using Vertex AI on Google Cloud.
1. Setting Up a Managed Notebook
The first step in using Vertex AI is setting up a managed notebook in workbench. You can create a new notebook through the Google Cloud Console under “Manage Notebooks.” Once the notebook is named, you can run it as a service account. In the advanced settings, users can customize the environment by adding GPUs for performance.
Workbench notebooks automatically shut down after inactivity to prevent unnecessary charges. The platform supports multi-kernel environments, meaning you can work with TensorFlow, PyTorch, R, or other custom environments within the same instance.
2. Storing Data in Cloud Storage
Data is central to machine learning, and Google Cloud’s Cloud Storage plays a key role. Training jobs in Vertex AI access data stored in Cloud Storage as if it were a local file system, using a path structure like `/gcs/your-bucket-name`. This setup ensures fast access to large datasets and allows you to easily save trained models back into Cloud Storage for later use.
3. Containerizing Code for ML Training
To scale up your machine learning experiments, Vertex AI provides the ability to containerize your training code. A container packages all the necessary dependencies, including the libraries and training code, which allows for easier deployment across various platforms.
Google Cloud offers pre-built containers for common use cases like TensorFlow or Scikit Learn. However, if your code has custom dependencies, you can build your own Docker containers. These containers enable training code to run anywhere, ensuring portability and flexibility for large-scale experiments.
4. Training the Model
Once the code and data are in place, the next step is to train the model. Vertex AI provides support for custom training jobs, which can be run using pre-built containers or a custom Docker image. The platform supports features such as hyperparameter tuning and distributed training, which are essential when optimizing the model for performance.
Hyperparameter tuning automates the search for the best configuration of model parameters, while distributed training allows you to train your model faster using multiple machines or GPUs.
5. Deploying the Model
After training, the model is ready for deployment. In Vertex AI, models are deployed through the Model Registry, which tracks the lifecycle of models. From the registry, users can deploy their models to an endpoint for making real-time predictions or use batch prediction for larger datasets.
Online predictions are useful when low-latency responses are required, while batch predictions process accumulated data asynchronously.
6. Making Predictions
Once deployed, models can generate predictions. Vertex AI allows predictions to be made either directly from a notebook or via an API call to the deployed endpoint. For online predictions, users can send data to the endpoint, which processes the request and returns the prediction. For batch predictions, large datasets are processed in a single request, making it ideal for projects that do not require immediate responses.
7. Experiment Tracking and Model Monitoring
Experimentation is a key part of the machine learning workflow, and Vertex AI provides experiment tracking services to help users monitor the performance of their models over time. TensorBoard is integrated into Vertex AI to visualize training metrics, track hyperparameter tuning results, and manage experiment data.
Google Cloud also offers additional tools for monitoring the health and performance of models in production, ensuring they continue to deliver accurate predictions over time.
Conclusion
With Google Cloud’s Vertex AI, the transition from prototype to production is streamlined. Vertex AI simplifies the development, training, and deployment of machine learning models, enabling businesses and data scientists to focus on improving model performance without being bogged down by infrastructure management. Whether you are experimenting with hyperparameters, distributing your training across multiple GPUs, or deploying models for real-time predictions, Vertex AI provides the tools to help you succeed in your machine learning journey.
Resource : Google Cloud Tech. Prototype to Production. https://goo.gle/PrototypeToProduction