AI & Cloud Infrastructure

Fine-Tuning in Microsoft Foundry: Building Production-Ready AI Agents - Microsoft Ignite 2025

By Technspire Team

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November 28, 2025

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4903 views

Baseline Performance Assessment (1-2 weeks)

• Identify use case requiring fine-tuning (tool calling, data extraction, workflow execution)
• Measure baseline with best-effort prompt engineering (accuracy, latency, cost)
• Define success criteria (target accuracy, latency, cost reduction)
• Estimate ROI (cost of fine-tuning vs. expected savings/value)
• Validate data availability (need 1,000+ high-quality examples)

Training Data Preparation (3-4 weeks)

• Collect real examples (historical data with known-good outputs)
• Annotate data with expert labels (correct tool calls, extracted fields, classifications)
• Use synthetic data generation to expand dataset (10× multiplier)
• Split data: 80% training, 10% validation, 10% test
• Format as JSONL (input-output pairs)
• Quality assurance: review samples, ensure consistency

Model Selection and Training (2-3 weeks)

• Choose base model (GPT-4o for accuracy, GPT-4o-mini for cost, Llama-3 for control)
• Run fine-tuning in Foundry (developer tier for experimentation)
• Hyperparameter tuning (learning rate, epochs, batch size)
• Monitor training metrics (loss curves, validation accuracy)
• Test multiple model versions (compare accuracy vs. cost trade-offs)
• Select best performer for production

Validation and Testing (2-3 weeks)

• Test on held-out test set (measure accuracy, latency, cost)
• Compare to baseline (is fine-tuned model significantly better?)
• Edge case testing (adversarial inputs, unusual formats, error conditions)
• User acceptance testing (domain experts validate quality)
• Performance benchmarking (throughput, concurrency, scaling behavior)
• Document evaluation results and model limitations

Production Deployment (2-3 weeks)

• Deploy fine-tuned model to Foundry inference endpoint
• Canary rollout (5% → 25% → 100% of traffic)
• Monitor production metrics (accuracy, latency, error rates)
• Set up alerting for degradation (accuracy drops, latency spikes)
• Implement fallback to baseline model if issues detected
• Track business metrics (cost savings, throughput, user satisfaction)

Continuous Improvement (Ongoing)

• Collect production data (new examples with errors to learn from)
• Periodic retraining (monthly or quarterly with updated data)
• A/B testing (compare new model versions vs. current production)
• Explore reinforcement fine-tuning (if complex reasoning needed)
• Model distillation (once large model proven, distill to smaller for cost)
• Measure ROI continuously (track savings vs. training investment)

Why This Matters for Swedish Organizations

Sweden's organizations face unique drivers for fine-tuning adoption:

Language requirements: Swedish language AI needs fine-tuning on Swedish text. Generic models trained primarily on English underperform on Swedish documents, terminology, cultural context.
Regulatory compliance: GDPR, NIS2, AI Act—fine-tuned models can be deployed on-premises or in EU data centers with full data control. Generic API models send data to US clouds.
Industry specialization: Swedish strengths (manufacturing, healthcare, fintech, cleantech) require domain-specific agents. Fine-tuning teaches models Swedish industry terminology and workflows.
Cost efficiency: Smaller Swedish organizations can't afford $33M/year AI bills. Fine-tuning enables 80-90% cost reduction through smaller, faster models.
Competitive advantage: Agents that understand your specific business processes execute faster and more accurately than competitors using generic models.
Data sovereignty: Training data stays in Sweden. Fine-tuned models deployed in Swedish Azure regions. No data leaves EU.

Key Takeaways from BRK188

✓ Fine-tuning transforms generic models into production-ready agents with 95%+ accuracy and 80-90% cost reduction
✓ Microsoft Foundry provides end-to-end platform: synthetic data generation, supervised + reinforcement fine-tuning, automated deployment
✓ Agentic Reinforcement Fine-Tuning (RFT) teaches models optimal tool usage and reasoning strategies (+9-19 point accuracy gains)
✓ Use case validation critical: Fine-tune when accuracy <90%, tool calling errors >5%, or high cost/latency
✓ Real-world results: Customer document management scaled to 2M docs/day, $27M annual savings, 98.7% accuracy
✓ Developer training tier enables low-cost experimentation before production deployment
✓ Open-source models supported: Llama, Mistral, Phi alongside Azure OpenAI for cost/control flexibility
✓ Organizations report 40-90% cost reduction and 3-7× throughput improvement with fine-tuned agents

Fine-tuning isn't optional for production agents—it's the difference between a demo that impresses and a system that delivers value. Microsoft Foundry makes fine-tuning accessible: synthetic data generation solves the training data challenge, reinforcement fine-tuning enables optimal reasoning, and automated deployment gets models to production fast. For Swedish organizations building agents that must handle Swedish language, comply with EU regulations, and operate cost-effectively at scale, fine-tuning in Foundry is the path from prototype to production.

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