What is Infrastructure as Code for ML?
Infrastructure as Code for ML is the practice of managing ML infrastructure through version-controlled, declarative configuration files enabling reproducible environments, automated provisioning, and consistent deployment across development, staging, and production systems.
This glossary term is currently being developed. Detailed content covering enterprise AI implementation, operational best practices, and strategic considerations will be added soon. For immediate assistance with AI operations strategy, please contact Pertama Partners for expert advisory services.
Infrastructure as Code reduces ML environment provisioning time from days to minutes and eliminates configuration drift that causes 30% of production ML incidents. Teams using IaC for ML infrastructure report 70% fewer environment-related debugging sessions and 5x faster disaster recovery. For companies managing ML workloads across multiple cloud regions in Southeast Asia, IaC ensures consistent deployments across Singapore, Tokyo, and Mumbai availability zones. The reproducibility guarantee also satisfies audit requirements for regulated industries.
- Tool selection (Terraform, Pulumi, CloudFormation) for ML workloads
- State management and infrastructure drift detection
- Secrets management and sensitive configuration handling
- Environment parity and configuration consistency
Common Questions
How does this apply to enterprise AI systems?
Enterprise applications require careful consideration of scale, security, compliance, and integration with existing infrastructure and processes.
What are the regulatory and compliance requirements?
Requirements vary by industry and jurisdiction, but generally include data governance, model explainability, audit trails, and risk management frameworks.
More Questions
Implement comprehensive monitoring, automated testing, version control, incident response procedures, and continuous improvement processes aligned with organizational objectives.
Use Terraform for cloud resource provisioning (GPU instances, storage, networking, managed ML services like SageMaker or Vertex AI). Use Kubernetes manifests or Helm charts for model serving deployment, scaling policies, and service mesh configuration. Use Pulumi if your team prefers Python over HCL for infrastructure definitions. For ML-specific abstractions, Kubeflow pipelines define training workflows as code, while MLflow Projects standardize experiment environments. Store all configurations in Git alongside model code. Layer these tools: Terraform provisions the cluster, Helm deploys the serving infrastructure, and pipeline definitions manage training workflows.
Follow a four-phase approach over 3-6 months: Phase 1 (weeks 1-4) document all existing infrastructure by importing current resources into Terraform state using terraform import, creating visibility without changing anything. Phase 2 (weeks 5-8) codify the most critical and frequently modified resources first (serving endpoints, training clusters). Phase 3 (weeks 9-12) implement CI/CD for infrastructure changes with plan review before apply. Phase 4 (ongoing) extend to remaining resources and implement policy-as-code using Open Policy Agent or Sentinel. Never attempt a big-bang migration; incremental adoption reduces risk and builds team familiarity gradually.
Use Terraform for cloud resource provisioning (GPU instances, storage, networking, managed ML services like SageMaker or Vertex AI). Use Kubernetes manifests or Helm charts for model serving deployment, scaling policies, and service mesh configuration. Use Pulumi if your team prefers Python over HCL for infrastructure definitions. For ML-specific abstractions, Kubeflow pipelines define training workflows as code, while MLflow Projects standardize experiment environments. Store all configurations in Git alongside model code. Layer these tools: Terraform provisions the cluster, Helm deploys the serving infrastructure, and pipeline definitions manage training workflows.
Follow a four-phase approach over 3-6 months: Phase 1 (weeks 1-4) document all existing infrastructure by importing current resources into Terraform state using terraform import, creating visibility without changing anything. Phase 2 (weeks 5-8) codify the most critical and frequently modified resources first (serving endpoints, training clusters). Phase 3 (weeks 9-12) implement CI/CD for infrastructure changes with plan review before apply. Phase 4 (ongoing) extend to remaining resources and implement policy-as-code using Open Policy Agent or Sentinel. Never attempt a big-bang migration; incremental adoption reduces risk and builds team familiarity gradually.
Use Terraform for cloud resource provisioning (GPU instances, storage, networking, managed ML services like SageMaker or Vertex AI). Use Kubernetes manifests or Helm charts for model serving deployment, scaling policies, and service mesh configuration. Use Pulumi if your team prefers Python over HCL for infrastructure definitions. For ML-specific abstractions, Kubeflow pipelines define training workflows as code, while MLflow Projects standardize experiment environments. Store all configurations in Git alongside model code. Layer these tools: Terraform provisions the cluster, Helm deploys the serving infrastructure, and pipeline definitions manage training workflows.
Follow a four-phase approach over 3-6 months: Phase 1 (weeks 1-4) document all existing infrastructure by importing current resources into Terraform state using terraform import, creating visibility without changing anything. Phase 2 (weeks 5-8) codify the most critical and frequently modified resources first (serving endpoints, training clusters). Phase 3 (weeks 9-12) implement CI/CD for infrastructure changes with plan review before apply. Phase 4 (ongoing) extend to remaining resources and implement policy-as-code using Open Policy Agent or Sentinel. Never attempt a big-bang migration; incremental adoption reduces risk and builds team familiarity gradually.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
- Google Cloud AI Infrastructure. Google Cloud (2024). View source
- Stanford HAI AI Index Report 2024 — Research and Development. Stanford Institute for Human-Centered AI (2024). View source
- NVIDIA AI Enterprise Documentation. NVIDIA (2024). View source
- Amazon SageMaker AI — Build, Train, and Deploy ML Models. Amazon Web Services (AWS) (2024). View source
- Azure AI Infrastructure — Purpose-Built for AI Workloads. Microsoft Azure (2024). View source
- MLflow: Open Source AI Platform for Agents, LLMs & Models. MLflow / Databricks (2024). View source
- Kubeflow: Machine Learning Toolkit for Kubernetes. Kubeflow / Linux Foundation (2024). View source
- Powering Innovation at Scale: How AWS Is Tackling AI Infrastructure Challenges. Amazon Web Services (AWS) (2024). View source
A TPU, or Tensor Processing Unit, is a custom-designed chip built by Google specifically to accelerate machine learning and AI workloads, offering high performance and cost efficiency for training and running large-scale AI models, particularly within the Google Cloud ecosystem.
A model registry is a centralised repository for storing, versioning, and managing machine learning models throughout their lifecycle, providing a single source of truth that tracks which models are in development, testing, and production across an organisation.
A feature pipeline is an automated system that transforms raw data from various sources into clean, structured features that machine learning models can use for training and prediction, ensuring consistent and reliable data preparation across development and production environments.
An AI gateway is an infrastructure layer that sits between applications and AI models, managing routing, authentication, rate limiting, cost tracking, and failover to provide centralised control and visibility over all AI model interactions across an organisation.
Model versioning is the practice of systematically tracking and managing different iterations of AI models throughout their lifecycle, recording changes to training data, parameters, code, and performance metrics so teams can compare, reproduce, and roll back to any previous version.
Need help implementing Infrastructure as Code for ML?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how infrastructure as code for ml fits into your AI roadmap.