· Machine Learning · 3 min read
Mastering Machine Learning on GCP: The Definitive Best Practices
From Vertex AI Workbench to MLOps pipelines Google's certified best practices for building scalable, production-grade ML systems.
Building a Machine Learning model is one thing; building a reliable, scalable ML system that drives business value is another.
Google Cloud recently released their comprehensive guide on Best practices for implementing machine learning on Google Cloud. It serves as a blueprint for engineering teams looking to move beyond “experimental” notebooks and into robust production environments.
At Alps Agility, we have distilled these practices into the core pillars that every Data & ML leader should strictly follow.
1. Environment: Treat Workbenches as Disposables
Gone are the days of manually configuring EC2 instances or maintaining fragile local environments.
- Use Vertex AI Workbench: Treat these instances as reproducible virtual workspaces.
- Isolation: Assign a unique instance for each team member to avoid dependency hell.
- Security: Leverage standardized, secure images rather than custom-built VMs that drift over time.
2. Data: The BigQuery Advantage
The “garbage in, garbage out” adage holds true, but where you store your data matters just as much as its quality.
- Structured Data: Must live in BigQuery. It’s serverless, scalable, and connects directly to Vertex AI for training without moving massive datasets.
- Unstructured Data: Images, audio, and video belong in Cloud Storage, organised into large container formats (like TFRecord) to maximise throughput.
- Feature Store: Use Vertex AI Feature Store to centralise feature logic, ensuring that the features used for training are identical to those used for serving (eliminating training-serving skew).
3. Training: Managed & Checkpointed
Training a model shouldn’t lock up your laptop or a random server in the closet.
- Managed Services: Run code in Vertex AI Training jobs. This allows for automatic scaling (adding GPUs on the fly) and burst capacity.
- Checkpoints: Always save training checkpoints to Cloud Storage. If a job crashes 90% of the way through, you shouldn’t lose days of compute time.
- Pipelines: operationalise training with Vertex AI Pipelines. Don’t rely on manual script execution.
4. Orchestration is Not Optional
To achieve true MLOps levels of maturity, manual steps must be eliminated.
- Vertex AI Pipelines: Use this to orchestrate the entire flow from data extraction to model validation.
- Kubeflow: We recommend using the Kubeflow Pipelines SDK for flexibility. It allows you to define your infrastructure as code, making your ML workflows versionable and reviewable just like your software applications.
How We Can Help
Implementing these best practices requires more than just reading the documentation it requires deep engineering expertise. At Alps Agility, we specialise in:
- Designing MLOps Architectures on Google Cloud.
- Migrating Legacy ML Models to Vertex AI.
- Building Feature Stores and scalable data pipelines.
Ready to modernise your ML stack? Contact our team today to discuss how we can bring Google-grade ML best practices to your organisation.
