Build ML Workflow Pipelines with Flyte and gRPC on Google Cloud C4A Axion processors
Introduction
Understand Flyte and gRPC ML workflows on Google Axion
Create a Google Axion C4A Arm virtual machine
Install Flyte and gRPC tools on Axion
Build a gRPC feature engineering service
Create ML Training Workflow
Execute and validate the ML pipeline
Understand the distributed ML architecture
Next Steps
Build ML Workflow Pipelines with Flyte and gRPC on Google Cloud C4A Axion processors
Introduction
Understand Flyte and gRPC ML workflows on Google Axion
Create a Google Axion C4A Arm virtual machine
Install Flyte and gRPC tools on Axion
Build a gRPC feature engineering service
Create ML Training Workflow
Execute and validate the ML pipeline
Understand the distributed ML architecture
Next Steps
Who is this for?
This is an introductory topic for developers, data engineers, and ML engineers who want to build scalable machine learning workflow pipelines on Arm64-based Google Cloud C4A Axion processors using Flyte workflow orchestration and gRPC-based microservices.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Deploy Flyte workflow pipelines on Google Cloud C4A Axion processors
- Build distributed machine learning pipelines using Flyte tasks
- Implement gRPC-based services for feature engineering
- Integrate Flyte workflows with distributed services
- Run scalable ML pipelines on Arm-based cloud infrastructure
Prerequisites
Before starting, you will need the following:
- A Google Cloud Platform (GCP) account with billing enabled
- Basic familiarity with Python
- Basic understanding of machine learning pipelines