Cloud computing provides the power and speed needed for Machine Learning (ML), and allows you to easily scale up and down. However, this also means that costs may spin out of control if you don’t plan ahead, which is especially fraught now, given that businesses are particularly cost conscious.
To use Google Cloud effectively for ML, then, it’s important to follow best practices to optimize for performance and costs. To help you do just that, we published a new set of best practices—based on our experience working with advanced ML customers on how you can enhance the performance and decrease the costs of your ML workloads on Google Cloud, from experimentation to production. The guide covers various Smart Analytics and Cloud AI services in different phases of the ML process, as illustrated in the diagram below, namely:
- Experimentation with AI Platform Notebooks
- Data preparation with BigQuery and Dataflow
- Training with AI Platform Training
- Serving with AI Platform Prediction
- Orchestration with AI Platform Pipelines
We also provide best practices for monitoring performance and managing the cost of ML projects with Google Cloud tools. Are you ready to optimize your ML workloads? Check out the Machine Learning Performance and Cost Optimization Best Practices to get started.
