What is ML Platform Roadmap?
ML Platform Roadmap is the strategic plan for ML infrastructure and capability development over time aligning platform evolution with business needs, technology trends, and organizational maturity through phased implementation and milestone tracking.
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.
Organizations without ML platform roadmaps accumulate technical debt that increases operational costs by 20-40% annually as model count grows. A structured roadmap prevents the common pattern of rebuilding infrastructure every 18 months when ad-hoc solutions fail at scale. For companies scaling from 3 to 30 ML models, roadmap-driven platform investment reduces per-model operational cost from $5,000 to under $1,000 monthly through standardization and automation.
- Stakeholder input and requirement gathering
- Phasing strategy balancing quick wins and long-term vision
- Resource allocation and dependency management
- Regular review and adaptation based on changing needs
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 a scoring framework weighing four dimensions: business impact (revenue protection or growth enabled), user pain severity (hours wasted weekly by ML practitioners), technical risk (security, compliance, scalability gaps), and strategic alignment (enabling future AI capabilities). Survey your ML practitioners quarterly to identify top friction points. Map platform capabilities against industry maturity models (Google MLOps maturity levels 0-5). Prioritize foundational capabilities (experiment tracking, CI/CD, monitoring) before advanced features (feature stores, AutoML, multi-tenant governance). Plan in 90-day increments with clear deliverables.
Phase 1 (months 1-3): centralized experiment tracking, basic model registry, and standardized development environments. Phase 2 (months 4-6): automated training pipelines, CI/CD for model deployment, and production monitoring dashboards. Phase 3 (months 7-9): feature store implementation, A/B testing infrastructure, and cost attribution per model. Phase 4 (months 10-12): self-service model deployment, automated retraining triggers, and governance automation. Each phase should include migration of existing ad-hoc workflows onto the platform, not just new capability development.
Use a scoring framework weighing four dimensions: business impact (revenue protection or growth enabled), user pain severity (hours wasted weekly by ML practitioners), technical risk (security, compliance, scalability gaps), and strategic alignment (enabling future AI capabilities). Survey your ML practitioners quarterly to identify top friction points. Map platform capabilities against industry maturity models (Google MLOps maturity levels 0-5). Prioritize foundational capabilities (experiment tracking, CI/CD, monitoring) before advanced features (feature stores, AutoML, multi-tenant governance). Plan in 90-day increments with clear deliverables.
Phase 1 (months 1-3): centralized experiment tracking, basic model registry, and standardized development environments. Phase 2 (months 4-6): automated training pipelines, CI/CD for model deployment, and production monitoring dashboards. Phase 3 (months 7-9): feature store implementation, A/B testing infrastructure, and cost attribution per model. Phase 4 (months 10-12): self-service model deployment, automated retraining triggers, and governance automation. Each phase should include migration of existing ad-hoc workflows onto the platform, not just new capability development.
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 MLOps — Continuous Delivery and Automation Pipelines. Google Cloud (2024). View source
- AI in Action 2024 Report. IBM (2024). View source
- MLflow: Open Source AI Platform for Agents, LLMs & Models. MLflow / Databricks (2024). View source
- Weights & Biases: Experiment Tracking and MLOps Platform. Weights & Biases (2024). View source
- ClearML: Open Source MLOps and LLMOps Platform. ClearML (2024). View source
- KServe: Highly Scalable Machine Learning Deployment on Kubernetes. KServe / Linux Foundation AI & Data (2024). View source
- Kubeflow: Machine Learning Toolkit for Kubernetes. Kubeflow / Linux Foundation (2024). View source
- Weights & Biases Documentation — Experiments Overview. Weights & Biases (2024). View source
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