What is ML Platform Evaluation?
ML Platform Evaluation is the systematic assessment of ML infrastructure solutions including cloud providers, MLOps platforms, and tools against technical requirements, cost constraints, scalability needs, and organizational capabilities to inform platform selection decisions.
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.
Poor ML platform selection wastes 6-12 months of engineering effort and $50,000-200,000 in migration costs when teams outgrow or become frustrated with their initial choice. Companies that follow structured evaluation processes select platforms achieving 80% team satisfaction compared to 40% satisfaction for ad-hoc selections. For Southeast Asian companies with limited ML platform experience, a rigorous evaluation prevents the common mistake of over-investing in enterprise platforms before the team is ready to utilize advanced features.
- Build vs buy vs hybrid platform decisions
- Vendor lock-in risk and migration strategies
- Total cost of ownership including hidden operational costs
- Integration with existing infrastructure and tools
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.
Evaluate across eight dimensions weighted by your organization's priorities: integration with existing infrastructure (cloud provider, data warehouse, CI/CD, weighted 20%), scalability (handles your projected model count and data volume growth for 2-3 years, 15%), ease of adoption (learning curve, documentation quality, community support, 15%), cost model (licensing, compute, storage, and scaling costs projected over 24 months, 15%), security and compliance (data encryption, access controls, audit logging, regulatory certifications, 15%), feature completeness (experiment tracking, model registry, serving, monitoring, 10%), vendor viability (funding, customer base, product roadmap, 5%), and extensibility (API access, custom integrations, plugin architecture, 5%). Run a 4-week proof of concept with 3 shortlisted platforms using a real project.
Adopt three defensive practices: use open formats for model artifacts (ONNX, PMML, or framework-native formats like PyTorch .pt files rather than proprietary formats), abstract platform-specific code behind internal interfaces so switching requires updating adapters rather than rewriting workflows, and maintain competency with open-source alternatives (MLflow, Kubeflow) even when using commercial platforms. Include data export and migration capabilities in your evaluation criteria. Negotiate contractual exit clauses covering data portability and transition support. Prefer platforms built on open-source cores (Databricks/MLflow, AWS SageMaker/open source SDKs) over fully proprietary systems. Review lock-in risk annually as part of platform health assessments.
Evaluate across eight dimensions weighted by your organization's priorities: integration with existing infrastructure (cloud provider, data warehouse, CI/CD, weighted 20%), scalability (handles your projected model count and data volume growth for 2-3 years, 15%), ease of adoption (learning curve, documentation quality, community support, 15%), cost model (licensing, compute, storage, and scaling costs projected over 24 months, 15%), security and compliance (data encryption, access controls, audit logging, regulatory certifications, 15%), feature completeness (experiment tracking, model registry, serving, monitoring, 10%), vendor viability (funding, customer base, product roadmap, 5%), and extensibility (API access, custom integrations, plugin architecture, 5%). Run a 4-week proof of concept with 3 shortlisted platforms using a real project.
Adopt three defensive practices: use open formats for model artifacts (ONNX, PMML, or framework-native formats like PyTorch .pt files rather than proprietary formats), abstract platform-specific code behind internal interfaces so switching requires updating adapters rather than rewriting workflows, and maintain competency with open-source alternatives (MLflow, Kubeflow) even when using commercial platforms. Include data export and migration capabilities in your evaluation criteria. Negotiate contractual exit clauses covering data portability and transition support. Prefer platforms built on open-source cores (Databricks/MLflow, AWS SageMaker/open source SDKs) over fully proprietary systems. Review lock-in risk annually as part of platform health assessments.
Evaluate across eight dimensions weighted by your organization's priorities: integration with existing infrastructure (cloud provider, data warehouse, CI/CD, weighted 20%), scalability (handles your projected model count and data volume growth for 2-3 years, 15%), ease of adoption (learning curve, documentation quality, community support, 15%), cost model (licensing, compute, storage, and scaling costs projected over 24 months, 15%), security and compliance (data encryption, access controls, audit logging, regulatory certifications, 15%), feature completeness (experiment tracking, model registry, serving, monitoring, 10%), vendor viability (funding, customer base, product roadmap, 5%), and extensibility (API access, custom integrations, plugin architecture, 5%). Run a 4-week proof of concept with 3 shortlisted platforms using a real project.
Adopt three defensive practices: use open formats for model artifacts (ONNX, PMML, or framework-native formats like PyTorch .pt files rather than proprietary formats), abstract platform-specific code behind internal interfaces so switching requires updating adapters rather than rewriting workflows, and maintain competency with open-source alternatives (MLflow, Kubeflow) even when using commercial platforms. Include data export and migration capabilities in your evaluation criteria. Negotiate contractual exit clauses covering data portability and transition support. Prefer platforms built on open-source cores (Databricks/MLflow, AWS SageMaker/open source SDKs) over fully proprietary systems. Review lock-in risk annually as part of platform health assessments.
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.
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