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Build ML Workflow Pipelines with Flyte and gRPC on Google Cloud C4A Axion processors
Signed-off-by: odidev <odidev@puresoftware.com>
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‎content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md‎

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---
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title: Build ML Workflow Pipelines with Flyte and gRPC on Google Cloud C4A Axion processors
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minutes_to_complete: 30
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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.
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learning_objectives:
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- Deploy Flyte workflow pipelines on Google Cloud C4A Axion processors
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- Build distributed machine learning pipelines using Flyte tasks
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- Implement gRPC-based services for feature engineering
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- Integrate Flyte workflows with distributed services
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- Run scalable ML pipelines on Arm-based cloud infrastructure
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prerequisites:
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- A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
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- Basic familiarity with Python
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- Basic understanding of machine learning pipelines
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- Familiarity with Linux command-line operations
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author: Pareena Verma
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##### Tags
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skilllevels: Introductory
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subjects: ML
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cloud_service_providers:
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- Google Cloud
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armips:
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- Neoverse
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tools_software_languages:
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- Flyte
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- Python
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- gRPC
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operatingsystems:
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- Linux
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# ================================================================================
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# FIXED, DO NOT MODIFY
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# ================================================================================
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further_reading:
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- resource:
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title: Google Cloud documentation
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link: https://cloud.google.com/docs
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type: documentation
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- resource:
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title: Flyte documentation
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link: https://docs.flyte.org/
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type: documentation
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- resource:
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title: gRPC documentation
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link: https://grpc.io/docs/
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type: documentation
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- resource:
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title: Flyte GitHub repository
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link: https://github.com/flyteorg/flyte
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type: documentation
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weight: 1
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layout: "learningpathall"
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learning_path_main_page: yes
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---

‎content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_next-steps.md‎

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# ================================================================================
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# FIXED, DO NOT MODIFY THIS FILE
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# ================================================================================
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weight: 21 # Set to always be larger than the content in this path to be at the end of the navigation.
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title: "Next Steps" # Always the same, html page title.
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layout: "learningpathall" # All files under learning paths have this same wrapper for Hugo processing.
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---

‎content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/architecture.md‎

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---
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title: ML Pipeline Architecture
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weight: 8
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### FIXED, DO NOT MODIFY
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layout: learningpathall
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---
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# ML Pipeline Architecture
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In this section, you explore the architecture behind the distributed machine learning pipeline built using Flyte and gRPC on Google Axion Arm-based infrastructure.
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This architecture demonstrates how modern ML workflows are orchestrated using workflow engines while delegating specific tasks to distributed services.
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Flyte manages the pipeline orchestration, while gRPC enables efficient communication between workflow tasks and external services.
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## System architecture
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The ML pipeline consists of several tasks executed sequentially within the Flyte workflow.
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```text
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Flyte Workflow Engine
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│
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▼
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Dataset Loader Task
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│
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▼
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Data Preprocessing Task
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│
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▼
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Feature Engineering Service (gRPC)
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│
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▼
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Model Training Task
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│
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▼
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Model Evaluation Task
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│
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▼
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Pipeline Result
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```
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Each component in the workflow performs a specific function within the machine learning pipeline.
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## Components
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### Flyte workflow engine
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Flyte orchestrates the pipeline execution. It manages task dependencies, workflow execution, and data flow between tasks.
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Key capabilities include:
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- defining ML pipelines as Python workflows
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- managing task dependencies
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- enabling reproducible ML experiments
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- scaling pipeline execution
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### Dataset loader
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The dataset loader task simulates loading a training dataset that will be used for model training.
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In real ML systems, this step might include:
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- loading datasets from object storage
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- retrieving data from data lakes
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- accessing distributed datasets
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### Data preprocessing
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Data preprocessing transforms raw data into a format suitable for model training.
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Typical preprocessing steps include:
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- cleaning data
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- normalizing values
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- handling missing data
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- encoding categorical variables
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### Feature engineering service (gRPC)
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Feature engineering is implemented as a gRPC microservice.
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This design allows feature-generation logic to run independently of the workflow engine.
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Benefits include:
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- scalable feature generation
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- reusable feature services
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- independent scaling of compute resources
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- low-latency communication using gRPC
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### Model training
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The training task uses generated features to train a machine learning model.
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In production systems, this stage might include:
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- training regression models
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- training classification models
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- training deep learning models
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### Model evaluation
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The evaluation step measures model performance.
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Typical evaluation metrics include:
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- accuracy
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- precision
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- recall
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- F1 score
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Based on the results, the workflow can determine whether to retrain the model.
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## Pipeline execution flow
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The ML pipeline follows this execution sequence.
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```text
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Load Dataset
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│
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▼
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Preprocess Data
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│
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▼
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Feature Engineering (gRPC Service)
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│
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▼
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Model Training
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│
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▼
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Model Evaluation
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│
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▼
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Pipeline Result
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```
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Each task executes sequentially while Flyte manages the workflow orchestration.
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## Benefits of this architecture
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This architecture provides several advantages:
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- scalable ML pipeline orchestration
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- distributed feature engineering services
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- modular pipeline components
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- efficient task communication using gRPC
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- reproducible machine learning workflows
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## Running on Axion
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This example demonstrates how machine learning workflows can run efficiently on Google Axion Arm-based processors.
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Benefits include:
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- high performance per watt
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- efficient execution of data pipelines
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- scalable infrastructure for ML workloads
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- optimized performance for modern cloud applications
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## What you've learned
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In this section, you explored the architecture behind the ML training pipeline.
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You learned how:
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- Flyte orchestrates ML workflows
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- gRPC services enable distributed feature engineering
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- pipeline tasks interact through workflow dependencies
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- ML pipelines can scale across distributed infrastructure
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This architecture underpins modern distributed machine learning systems running on Arm-based cloud infrastructure.

‎content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/background.md‎

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---
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title: Get started with Flyte ML Workflow Pipelines with gRPC on Google Axion C4A
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weight: 2
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layout: "learningpathall"
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---
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## Explore Axion C4A Arm instances in Google Cloud
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Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse-V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for data-intensive and analytics workloads such as big data processing, in-memory analytics, columnar data processing, and high-throughput data services.
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The C4A series provides a cost-effective alternative to x86 virtual machines while leveraging the scalability, SIMD acceleration, and memory bandwidth advantages of the Arm architecture in Google Cloud.
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These characteristics make Axion C4A instances well-suited for modern analytics stacks that rely on columnar data formats and memory-efficient execution engines.
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To learn more, see the Google blog [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu).
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## Explore Flyte ML Workflow Pipelines with gRPC on Google Axion C4A (Arm Neoverse V2)
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Flyte is an open-source workflow orchestration platform used to build scalable and reproducible data and machine learning pipelines. It allows developers to define workflows as Python tasks, simplifying the management of complex ML processes such as data preparation, feature engineering, and model training.
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gRPC enables fast communication between distributed services within these pipelines. Running Flyte with gRPC on Google Axion C4A Arm-based processors provides efficient, scalable infrastructure for executing modern ML workflows and distributed data processing tasks.
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To learn more, visit the [Flyte documentation](https://docs.flyte.org/) and explore the [gRPC documentation](https://grpc.io/docs/) to understand how distributed service communication enables scalable machine learning workflows.
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## What you've learned and what's next
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In this section, you learned about:
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* Google Axion C4A Arm-based VMs and their performance characteristics
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* Flyte as a workflow orchestration platform for machine learning pipelines
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* gRPC as a communication layer for distributed services
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* How Flyte and gRPC can be used together to build scalable ML training pipelines
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Next, You will deploy Flyte tools, create a gRPC-based feature engineering service, and build a distributed ML workflow pipeline that orchestrates data processing and model training tasks on Axion infrastructure.

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