Forum Discussion
How to build AI Research Team
As I plan to establish an AI Research Lab dedicated to enterprise-grade LLM development, what are the end-to-end best practices for:
- Setting up the lab’s infrastructure, governance and research processes;
- Assembling an optimal multidisciplinary team structure (e.g., research scientists, ML engineers, data engineers, MLOps specialists, security and compliance officers);
- Sourcing and purchasing high‑quality, domain‑specific enterprise datasets—including typical marketplaces, licensing models, pricing considerations, and budget planning;
- Defining the key output criteria for an enterprise LLM—covering performance metrics (latency, throughput, accuracy), scalability, robustness, interpretability, bias mitigation, security, and regulatory compliance?
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hello nttien94 recently, I have done some AI research focusing on Microsoft Azure, sharing it here, if it useful for you.
a.Setting Up Lab Infrastructure & Governance on Azure
Infrastructure (Azure-native)
- Compute
- Azure AI Studio + Azure Machine Learning for training, fine-tuning, and experimentation.
- Azure NDv5 / NCas T4 v3 GPU VMs or Azure OpenAI for managed access to LLMs.
- Azure Kubernetes Service (AKS) for large-scale training/inference pipelines.
- Azure Batch for distributed training jobs.
- Data & Storage
- Azure Data Lake Storage Gen2 for raw + curated datasets.
- Azure Synapse / Fabric for integration with enterprise data sources.
- Confidential Computing (DCsv3/DCasv5) for sensitive data.
- Networking & Security
- VNet-isolated environments (no public endpoints).
- Azure Private Link + Key Vault + Managed Identities.
- Enforce Zero Trust and conditional access for researchers.
Governance & Processes
- Experiment tracking → MLflow (integrated with Azure ML).
- Model registry → Azure ML Model Registry with lineage & versioning.
- Responsible AI → Adopt Microsoft’s Responsible AI Standard: fairness, reliability, privacy, inclusiveness.
- Research workflows
- Sandbox → Controlled staging → Production research clusters.
- Reproducibility through notebooks, pipelines, and GitOps.
- Weekly reviews on ethical, technical, and business impact.
b.Multidisciplinary Team Structure
Core Roles
- Research Scientists ,ML Engineers ,Data Engineers ,MLOps Specialists ,Security & Compliance Officers ,Domain SMEs ,Program Manager ,
Optional/Advanced: Prompt Engineers / Applied Scientists for RAG & task-specific tuning. / UX Researchers to study AI system usability & trust.
c.Sourcing & Purchasing Enterprise Datasets
Sources
- Azure Marketplace → Pre-licensed datasets across finance, healthcare, manufacturing.
- Commercial Data Providers → LexisNexis, Bloomberg, Refinitiv, Elsevier.
- Synthetic Data → Generated with Azure AI synthetic data service for privacy-sensitive use cases.
Licensing Models
- Subscription (annual) → Access to continuously updated datasets.
- One-time purchase → Frozen dataset for specific domain study.
- Usage-based (API) → Charged per query/record (e.g., financial tick data).
Budget Planning
- Reserve 30–40% of research budget for data acquisition & curation.
- Account for storage + egress costs in Azure (especially large corpora).
- Negotiate enterprise licenses to allow fine-tuning and derivative works.
d.Defining Output Criteria for Enterprise LLMs
Performance Metrics
Scalability & Robustness
Interpretability & Bias Mitigation
Security & Compliance
Regulatory & Trust
e.Recommended Sequencing (Phased Rollout)
- Phase 1 – Foundation: Secure Azure environment, storage, networking, RBAC.
- Phase 2 – Data: Acquire datasets, build pipelines, establish governance.
- Phase 3 – People: Assemble team + define research workflows.
- Phase 4 – Models: Start with open-source LLMs (Phi-3, LLaMA-3, Mistral) fine-tuned in Azure.
- Phase 5 – Evaluation: Define KPIs, bias testing, compliance reviews.
- Phase 6 – Scale: Deploy enterprise-ready inference endpoints + continuous monitoring.
This gives you a high-level playbook for building an Azure-focused enterprise AI research lab.
