Training and Deploying Custom Reasoning Models with Azure ML and Foundry

See the magic happen in real time. Microsoft Ignite 2025 session BRK210 demonstrated training and deploying custom reasoning models with Azure ML and Microsoft Foundry—from fine-tuning to reinforcement learning, performance optimization, and production deployment. Learn how to bring measurable ROI with custom AI models to your own projects.

From Prototype to Production: The AI Innovation Challenge

Organizations across industries are driving measurable ROI using Microsoft Foundry and Azure Machine Learning. The session showcased AI innovation from prototype to production, emphasizing efficiency, scalability, and real business value.

The Journey from Experimentation to Production

→ Prototype phase: Rapid experimentation with pre-built models, proof of concept development, initial accuracy testing

→ Customization phase: Fine-tuning for domain-specific data, model optimization, performance benchmarking

→ Production deployment: Scalable infrastructure, monitoring and observability, continuous improvement

→ ROI realization: Measurable business outcomes, cost optimization, operational efficiency gains

✅ Technspire Perspective: Custom Model ROI

A Swedish insurance company experimented with generic GPT-4 for claims processing—achieving 72% accuracy on claim categorization and 65% on fraud detection. While promising, these numbers weren't sufficient for production deployment. After fine-tuning a custom model on their 5 years of historical claims data (280,000 claims) using Azure ML and Foundry, accuracy jumped to 94% for categorization and 89% for fraud detection. The fine-tuned model understood industry-specific terminology (Swedish insurance jargon), recognized regional fraud patterns, and handled edge cases that generic models missed. Production deployment reduced average claims processing time from 4.5 days to 18 hours, decreased manual review requirements by 67%, and caught €2.3M in fraudulent claims in the first 6 months that would have been missed by the generic model. The custom model investment of €45,000 paid for itself in 3 months through fraud prevention alone—not counting the operational efficiency gains.

Microsoft Foundry: Comprehensive AI Platform

Foundry was highlighted as a comprehensive platform integrating pre-built models, governance, and observability. It enables rapid debugging, deployment, and trust in AI systems—helping teams move from experimentation to production seamlessly.

Foundry Platform Capabilities

🤖 Pre-Built Model Catalog

Access to 11,000+ models from OpenAI, Anthropic, Meta, Google, and specialized providers—start with best-in-class models before customization

🔧 Custom Model Training

Fine-tuning infrastructure with automated data preprocessing, hyperparameter optimization, and distributed training across GPU clusters

🛡️ Governance & Compliance

Built-in policy enforcement, audit trails, data residency controls, and integration with Microsoft Purview for regulatory compliance

👁️ Observability & Monitoring

Real-time performance metrics, model drift detection, latency tracking, and cost monitoring across all deployed models

🔍 Debugging Tools

Trace inference paths, analyze failure modes, compare model versions, and identify data quality issues causing poor predictions

Azure Machine Learning Integration

Azure Machine Learning (Azure ML) enhances model training, evaluation, and deployment—supporting custom models and fine-tuning within Kubernetes clusters for optimized performance and resource control.

Azure ML Capabilities for Custom Models

Distributed Training

Automatically scale training across multiple GPUs and nodes—reducing training time from days to hours for large models

Automated Hyperparameter Tuning

Intelligent search across hyperparameter space to find optimal learning rates, batch sizes, and model architectures

Model Evaluation Framework

Automated benchmarking on test datasets with accuracy, precision, recall, F1 scores, and domain-specific metrics

Kubernetes Deployment

Deploy models to AKS (Azure Kubernetes Service) with auto-scaling, load balancing, and blue-green deployments for zero-downtime updates

Resource Optimization

Intelligent GPU allocation, spot instance usage for cost savings, and automatic resource cleanup to minimize waste

Fine-Tuning: Beyond Generic Models

Demonstrations illustrated advantages of fine-tuning over traditional retrieval methods—improving precision, domain-specific adaptation, and reliability.

🔍 RAG (Retrieval-Augmented Generation)

Approach: Generic model + external knowledge retrieval

Pros:

• No model training required
• Easy to update knowledge base
• Works with any foundation model

Cons:

• Limited by retrieval quality
• Higher latency (search + inference)
• Struggles with nuanced reasoning
• Can hallucinate beyond retrieved context

🎯 Fine-Tuning

Approach: Custom-trained model on domain data

Pros:

• Domain expertise embedded in weights
• Faster inference (no retrieval step)
• Better reasoning on domain problems
• More reliable outputs

Cons:

• Requires training data and compute
• Updates require retraining
• Initial investment higher

When to Fine-Tune vs. Use RAG

→ Fine-tune when: You have domain-specific language, specialized reasoning patterns, or need consistent high accuracy on specific task types
→ Use RAG when: Knowledge changes frequently, you need to cite sources, or you're working with general-purpose queries across diverse domains
→ Combine both: Fine-tune a base model on your domain, then use RAG to inject latest information—best of both approaches

Reinforcement Fine-Tuning (RFT): Elevating Reasoning

Reinforcement Fine-Tuning (RFT) techniques were demonstrated to elevate model reasoning and accuracy efficiently—training models to think through problems step-by-step.

How Reinforcement Fine-Tuning Works

Step 1: Supervised Fine-Tuning (SFT)

Train model on high-quality examples showing correct reasoning steps and final answers

Step 2: Generate Candidates

Model generates multiple solution attempts for each problem, exploring different reasoning paths

Step 3: Reward Modeling

Evaluate which solutions are correct/better using automated verifiers or human feedback

Step 4: Reinforcement Learning

Update model weights to increase probability of generating high-reward (correct) solutions

Step 5: Iterative Refinement

Repeat the process, progressively improving reasoning quality and solution accuracy

Result: RFT produces models that not only get the right answer but show their reasoning—critical for domains like healthcare, finance, and legal where explainability matters.

Performance Optimization: Speed and Cost

Strategies such as speculative decoding, distillation, and quantization were explained to accelerate inference, reduce cost, and enhance throughput.

Optimization Techniques

Speculative Decoding

Use fast "draft" model to generate candidate tokens, validate with full model in parallel—2-3x faster inference

Best for: High-throughput scenarios where latency matters more than cost

Model Distillation

Train smaller "student" model to mimic large "teacher" model—10x smaller, 5x faster, 90-95% of accuracy

Best for: Production deployment where cost and latency are critical

Quantization

Reduce model precision from FP32 to INT8 or INT4—4-8x less memory, 2-4x faster, minimal accuracy loss

Best for: Edge deployment or GPU-constrained environments

Batch Processing

Group multiple inference requests together—better GPU utilization, higher throughput, lower cost per request

Best for: Asynchronous workloads where individual request latency is flexible

Caching

Store and reuse results for common queries—instant responses, zero compute cost for cache hits

Best for: Repetitive queries or queries with common prefixes

✅ Technspire Perspective: Optimization Impact

A Swedish e-commerce company deployed a custom product recommendation model that cost €12,000 monthly in Azure OpenAI API calls (GPT-4 for 2M daily recommendations). We implemented three optimizations: (1) Model distillation—trained a smaller model that maintained 93% of GPT-4's accuracy but ran 8x faster, (2) Quantization—reduced model size by 75% allowing more instances per GPU, (3) Intelligent caching—cached recommendations for popular product categories and user segments. Combined impact: Cost dropped from €12,000 to €1,800 monthly (85% reduction), average inference latency improved from 420ms to 85ms (5x faster), and recommendation throughput increased from 2M to 8M daily requests on the same infrastructure. The optimizations paid for themselves in the first month through infrastructure savings alone—not counting the revenue impact of faster, more personalized recommendations.

Combining Draft and Base Models: Speculative Decoding

The session demonstrated how combining draft and base models delivers faster, high-quality AI responses at scale through speculative decoding.

Speculative Decoding Workflow

1. Draft generation: Fast, small model generates 4-8 candidate tokens speculatively

2. Parallel validation: Large base model evaluates all candidates simultaneously in one forward pass

3. Accept or reject: Keep valid tokens from draft, reject and regenerate invalid ones with base model

4. Repeat: Continue until full response generated—2-3x faster than base model alone

Key advantage: You get the quality of the large model with the speed closer to the small model—without compromising accuracy.

Real-Time Demonstration: Training to Deployment

The session included real-time demonstrations showing the complete workflow from data preparation through training, evaluation, optimization, and production deployment.

📊 Data Preparation

Live demonstration of:

• Importing training dataset to Azure ML
• Automated data quality checks
• Train/validation/test split configuration
• Data augmentation for small datasets

⚙️ Model Training

Real-time training showed:

• Distributed training across 4 GPUs
• Live loss/accuracy metrics
• Automatic checkpointing
• Early stopping to prevent overfitting

📈 Evaluation

Model evaluation demonstrated:

• Automated benchmarking on test set
• Comparison with baseline models
• Error analysis and failure modes
• Performance vs. cost tradeoffs

🚀 Deployment

Production deployment showed:

• One-click deployment to AKS
• Auto-scaling configuration
• A/B testing setup
• Monitoring dashboard activation

Implementation Roadmap: Custom Model Development

Organizations should approach custom model development with Azure ML and Foundry systematically:

Phase 1: Baseline Establishment (Weeks 1-2)

Test generic models on your data, establish accuracy baselines, identify gaps and weaknesses

Phase 2: Data Collection & Preparation (Weeks 3-5)

Gather training data, clean and label datasets, create train/validation/test splits

Phase 3: Fine-Tuning Experiments (Weeks 6-9)

Train multiple model variants, tune hyperparameters, compare performance vs. baselines

Phase 4: Optimization (Weeks 10-12)

Apply distillation, quantization, caching—optimize for production performance and cost

Phase 5: Production Deployment (Weeks 13-15)

Deploy to Kubernetes, implement monitoring, establish retraining pipelines, measure ROI

Phase 6: Continuous Improvement (Ongoing)

Monitor model drift, collect feedback, retrain periodically, expand to new use cases

Key Takeaway: Measurable Business Value

Microsoft Foundry, coupled with Azure ML, streamlines creation and optimization of intelligent agents—enabling organizations to deploy secure, customized, and high-performing AI solutions that deliver tangible business value.

✓ Comprehensive platform: Pre-built models, custom training, governance, and observability in one place
✓ Enterprise-grade infrastructure: Distributed training, Kubernetes deployment, auto-scaling for production workloads
✓ Fine-tuning advantages: Domain expertise, faster inference, better reasoning than generic models + retrieval
✓ Reinforcement learning: Train models to reason step-by-step with explainable outputs
✓ Performance optimization: Speculative decoding, distillation, quantization reduce costs by 60-85%
✓ Measurable ROI: Organizations achieving 3-6 month payback periods through operational efficiency and accuracy improvements

The session made clear: custom models aren't just for research—they're production-ready solutions delivering measurable business outcomes when implemented with the right platform and optimization strategies.

Ready to Build Custom AI Models with Azure ML and Foundry?

Technspire helps Swedish and European organizations develop, optimize, and deploy custom reasoning models using Azure ML and Microsoft Foundry. From data preparation to production deployment, we ensure your custom models deliver measurable ROI with enterprise-grade performance and security.

Schedule a Free Consultation Watch Full Session

Contact us to discuss how custom model development with Azure ML and Foundry can improve accuracy, reduce costs, and accelerate AI innovation in your organization.

Key Takeaways from Microsoft Ignite BRK210

• Microsoft Foundry integrates pre-built models, custom training, governance, and observability in one platform
• Azure ML provides distributed training, automated tuning, evaluation, and Kubernetes deployment infrastructure
• Fine-tuning improves precision, domain adaptation, and reliability beyond RAG approaches
• Reinforcement Fine-Tuning (RFT) trains models to reason step-by-step with explainable outputs
• Speculative decoding combines draft and base models for 2-3x faster inference without quality loss
• Model distillation creates 10x smaller models maintaining 90-95% of original accuracy
• Quantization reduces memory 4-8x and speeds inference 2-4x with minimal accuracy loss
• Real-time demonstrations showed complete workflow from data prep to production deployment
• Organizations achieving 60-85% cost reductions and 3-6 month ROI through custom model optimization

Training and Deploying Custom Reasoning Models with Azure ML and Foundry - Microsoft Ignite 2025