1 min read
Implementing Feature Stores with Microsoft Fabric OneLake
Without a feature store, teams often duplicate feature engineering work across projects. Features computed for training may differ from inference, causing…
MLOps
88 articles
1 min read
Building Production-Ready AI Pipelines with Azure Machine Learning
Many organizations struggle with the gap between experimentation and production. Models that work in notebooks often fail in real-world scenarios due to…
1 min read
Testing AI Applications: Strategies for LLM-Based Systems
Test AI components within the full application context, including error handling, timeout behavior, and integration with downstream systems.
1 min read
Monitoring AI Applications with Azure Monitor and Application Insights
Configure alerts for latency spikes, error rate increases, and token usage anomalies to catch issues before they impact users.
1 min read
Azure Machine Learning Pipelines: Orchestrating ML Workflows
Configure pipelines to run on schedules or in response to data changes, enabling fully automated ML operations with minimal manual intervention.
1 min read
Model Evaluation Techniques: Beyond Accuracy Metrics
Always connect model metrics to business outcomes. A model with lower accuracy but better performance on high-value segments may deliver more business value…
1 min read
Azure Machine Learning Managed Endpoints: Advanced Deployment Patterns
Configure autoscaling rules that balance responsiveness with cost for production deployments while maintaining service level objectives.
1 min read
Enterprise AI Governance: Building Model Registries and Approval Workflows
A model registry serves as the single source of truth for all ML models, tracking versions, lineage, and deployment status.
1 min read
LLMOps: Operationalizing Large Language Models in Production
LLMs present distinct operational challenges: non-deterministic outputs, prompt sensitivity, context window management, and the difficulty of defining…
1 min read
Building Production MLOps Pipelines with Azure Machine Learning
Automated pipelines ensure reproducibility, enable A/B testing, and provide audit trails for model governance.
1 min read
End-to-End MLOps with Azure Machine Learning and GitHub Actions
Implement approval gates before production deployment. Track model lineage, performance metrics, and data dependencies for full auditability.
1 min read
Azure Machine Learning Managed Endpoints: Deploying Models with Zero Downtime
Configure Azure Monitor alerts on latency and error rates. Implement automatic rollback triggers when thresholds are breached.
1 min read
Fine-Tuning GPT-4o for Domain-Specific Tasks on Azure
After training completes, evaluate on a held-out test set before deploying. Monitor the fine-tuned model's performance against the base model to ensure…
1 min read
Fabric Data Pipelines: Orchestrating ML Feature Engineering at Scale
For real-time inference, query the feature store directly from your model serving endpoint. Fabric's query optimization ensures millisecond-level feature…
1 min read
AI Drift Detection: Monitoring Model Performance Over Time
Proactive drift detection prevents silent AI performance degradation.
1 min read
Model Versioning: Managing AI Model Lifecycle
Robust model versioning enables confident deployments and quick rollbacks.
1 min read
LLMOps: Operationalizing Large Language Models
LLMOps ensures reliable, cost-effective LLM operations at scale.
1 min read
Databricks AI: Unified Data and AI Platform
Databricks unifies data, ML, and AI on a single lakehouse platform.
1 min read
Model Optimization Techniques: From Training to Deployment
Model optimization is both science and art. Start with the techniques that offer the best impact for your specific constraints.
1 min read
Efficient Inference: Optimization Techniques for Production AI
Efficient inference is crucial for production AI. Apply these techniques systematically and measure the impact at each step.
1 min read
Edge AI Improvements: Deploying Intelligence at the Data Source
Edge AI Improvements: Deploying Intelligence at the Data Source
1 min read
MLOps Maturity: Productionizing Machine Learning
MLOps is essential for sustainable ML in production. Start with experiment tracking and gradually add components as your ML practice matures.
1 min read
Production AI Challenges: What They Don't Tell You in Tutorials
Production AI is hard. These challenges require dedicated engineering effort, not just model selection. Plan for them from the start.
1 min read
Online Inference Patterns: Low-Latency ML Predictions at Scale
Online inference requires careful attention to latency, reliability, and scalability. Choose patterns based on your specific requirements and constraints.
1 min read
Streaming Machine Learning: Training and Inference on Real-Time Data
Streaming ML enables intelligent real-time systems that continuously learn and adapt. Start with robust feature engineering and monitoring before enabling…
1 min read
Custom Model Deployment on Azure: From Fine-Tuning to Production
Best for: Variable workloads, quick start, minimal ops overhead Best for: Predictable workloads, SLA requirements, cost optimization
1 min read
MLflow Tracing for LLM Applications: Open Source Observability
MLflow provides a solid open-source foundation for LLM observability. Its strength lies in the familiar MLOps workflow and integration with the broader ML…
1 min read
Weights and Biases for LLM Applications: Complete Guide
Weights & Biases provides a comprehensive platform for LLM observability, evaluation, and collaboration. Its strength lies in combining experiment tracking…
1 min read
Azure AI Studio Updates at Build 2024
Azure AI Studio now provides a single pane of glass for: Model catalog browsing Prompt engineering Fine tuning Evaluation Deployment Monitoring
1 min read
Serverless Model Serving: Pay-Per-Request ML Inference
Serverless model serving eliminates infrastructure management while providing cost-effective, scalable ML inference. This guide covers implementing…
1 min read
Databricks Model Serving Updates: April 2024
Databricks Model Serving continues to evolve with new features for deploying and scaling ML models. This guide covers the latest updates and best practices.
1 min read
Online Feature Serving in Databricks: Real-Time ML Features
Online feature serving enables real-time ML inference by providing low-latency access to features. This guide covers setting up and using Databricks online…
1 min read
Feature Engineering in Databricks: From Raw Data to ML Features
Feature engineering transforms raw data into meaningful inputs for machine learning models. Databricks provides powerful tools for building and managing…
1 min read
Unity Catalog for ML: Governed Machine Learning
Unity Catalog extends governance to machine learning assets. Manage models, features, and experiments with the same rigor as your data.
1 min read
Monitoring AI Systems in Production
AI systems require specialized monitoring beyond traditional application metrics. This guide covers comprehensive observability for production AI.
1 min read
Gradual Rollout Strategies for AI Features
Deploying AI features to production requires careful risk management. Gradual rollouts help identify issues early while minimizing blast radius.
1 min read
Feature Flags for AI: Controlling AI Rollouts
Feature flags provide fine-grained control over AI features, enabling safe deployments, quick rollbacks, and targeted releases. This guide covers…
1 min read
A/B Testing AI Features: Data-Driven Model Selection
A/B testing AI features requires special considerations beyond traditional web experiments. This guide covers how to design, implement, and analyze AI…
1 min read
End-to-End RAG Evaluation: Complete System Assessment
Individual component metrics tell part of the story, but end-to-end evaluation measures how well your entire RAG pipeline performs as a system.
1 min read
Model Evaluation Frameworks: A Comprehensive Guide
Rigorous model evaluation is critical for production AI systems. This guide covers the major evaluation frameworks and how to implement comprehensive…
1 min read
Custom Model Deployment on Azure: From Training to Production
Deploying custom AI models to production requires careful consideration of scalability, reliability, and cost. This guide covers the complete journey from…
7 min read
Leakage-Safe Feature Engineering with scikit-learn Pipelines
Most feature engineering bugs are statistics fitted on the wrong rows. How I keep scaling, encoding and aggregates leak-free with scikit-learn pipelines.
7 min read
Azure ML in Early 2024: What Went GA at Ignite and What to Adopt
Feature store, serverless compute, prompt flow and the model catalog went GA in Azure ML in November 2023. Here's what to adopt now and what to hold off on.
1 min read
Model Risk Management for Large Language Models
LLMs are different beasts: unpredictable, context‑sensitive, and often opaque. Model risk management for LLMs needs to emphasise provenance, prompt…
1 min read
ML Model Deployment Patterns in Microsoft Fabric
Best practices and patterns for deploying machine learning models in Microsoft Fabric.
1 min read
The PREDICT Function in Microsoft Fabric
Using the PREDICT function to apply machine learning models directly in Microsoft Fabric for seamless predictions.
1 min read
AI Integration in Microsoft Fabric
Exploring AI and machine learning capabilities within Microsoft Fabric for intelligent analytics.
1 min read
Experiment Tracking in Fabric: Best Practices and Patterns
Tomorrow we'll explore Azure OpenAI Code Interpreter. MLflow Tracking Experiment Best Practices MLOps Patterns
1 min read
ML Models in Fabric: Training, Registry, and Deployment
Tomorrow we'll explore MLflow integration in Fabric. Model Management in Fabric MLflow Model Registry Model Deployment Guide
1 min read
Azure Machine Learning Updates at Build 2023
Azure ML continues to evolve as a comprehensive platform for both traditional ML and GenAI workloads. Tomorrow, I will cover Responsible AI improvements in…
1 min read
Prompt Flow General Availability: Building Production LLM Applications
Prompt Flow provides the foundation for building production-grade LLM applications. Tomorrow, I will cover Azure Machine Learning updates from Build 2023.
1 min read
Spark ML Patterns for Production Systems
Spark ML provides battle-tested patterns for production machine learning. From feature engineering to model persistence, these patterns ensure reliable ML…
1 min read
Integrating Azure Machine Learning with Azure OpenAI
The combination of Azure ML's operational capabilities with Azure OpenAI's language understanding creates powerful, production-ready AI systems.
1 min read
Introduction to LLMOps: MLOps for Large Language Models
LLMOps brings discipline to LLM application development. Start with these foundations and iterate as your applications mature.
1 min read
Azure Machine Learning Updates from Microsoft Ignite 2022
Feature stores are crucial for ML operations. Azure ML now includes a managed feature store: The Responsible AI dashboard now includes more capabilities:…
1 min read
Building Reusable Components in Azure ML
Well-designed components enable team collaboration and accelerate ML development through reuse.
1 min read
Azure ML Pipelines v2: Modern ML Workflows
Azure ML Pipelines v2 provides a modern, Pythonic way to build production-ready ML workflows.
1 min read
Prediction Drift Monitoring for ML Models
Prediction drift monitoring provides early warning of model issues without waiting for ground truth labels.
1 min read
Feature Drift Monitoring for ML Systems
Feature-level drift monitoring enables targeted investigation and remediation of model issues.
1 min read
Concept Drift Detection in Machine Learning
Detecting concept drift enables timely model retraining to maintain prediction accuracy over time.
1 min read
Data Drift Detection for ML Models
Early drift detection enables proactive model maintenance and prevents silent failures in production.
1 min read
Model Monitoring in Production with Azure ML
Comprehensive monitoring ensures your ML models maintain their performance and reliability in production.
1 min read
A/B Testing for ML Models in Production
A/B testing provides statistical evidence for model selection based on actual business outcomes.
1 min read
Canary Deployments for ML Models
Canary deployment provides a controlled, observable approach to rolling out new model versions with minimal risk.
1 min read
Blue-Green Deployment for ML Models
Blue-green deployment provides a safe, zero-downtime approach to updating ML models in production.
1 min read
Azure ML Managed Online Endpoints for Model Deployment
Managed online endpoints simplify model deployment while providing enterprise-grade reliability and scalability.
1 min read
Azure Machine Learning Updates and New Features
Azure Machine Learning SDK v2 provides a cleaner, more intuitive API for the complete ML lifecycle.
1 min read
MLflow Model Registry: Versioning and Deploying ML Models
The registry organizes models with: Registered Models : Named model artifacts Model Versions : Specific iterations of a model Stages : Lifecycle states…
1 min read
Databricks Model Serving: Real-Time ML Inference at Scale
Key features: Serverless : No infrastructure management Auto scaling : Handles variable traffic automatically Low latency : Sub second response times…
1 min read
Databricks Feature Store: Centralized Feature Management for ML
The Feature Store solves these problems.
1 min read
Databricks ML: End-to-End Machine Learning on the Lakehouse
The ML platform includes: Feature Store : Centralized feature management AutoML : Automated model training and selection MLflow : Experiment tracking and…
1 min read
CI/CD for Machine Learning: Building Reliable ML Pipelines
ML CI/CD in 2021 became essential for production systems. The tooling caught up with the need, and now there's no excuse for manual deployments.
1 min read
Model Monitoring in Production: Detecting Drift Before It's Too Late
Model monitoring in 2021 became non-negotiable for production ML. The tools improved, but the discipline of continuous monitoring is what separates…
1 min read
Feature Engineering Best Practices for Production ML
Feature engineering in 2021 became more systematic and production-oriented. The ad-hoc notebook approach is giving way to proper engineering practices.
1 min read
AI and Machine Learning Trends: What 2021 Taught Us
2021 proved that AI is no longer experimental - it's infrastructure. The focus has shifted from "can we do ML?" to "how do we do ML responsibly and reliably?"
2 min read
Repos in Databricks: Managing Production Deployments
Databricks Repos in production use means the code running in your prod workspace is explicitly linked to a specific Git commit—not "whatever notebooks…
1 min read
Batch Inference at Scale with Azure ML Batch Endpoints
Batch endpoints enable cost-effective, scalable inference for large datasets without the complexity of managing infrastructure.
1 min read
Deep Dive into Azure ML Managed Online Endpoints
Managed online endpoints provide a robust, production-ready platform for serving ML models with enterprise-grade features out of the box.
1 min read
Deploying Machine Learning Models with Azure ML
Model deployment is where your ML work delivers business value. Azure ML's managed endpoints make it straightforward to deploy, scale, and update models in…
1 min read
Model Management with Azure ML Model Registry
The Model Registry is the cornerstone of production ML. It provides the governance and traceability needed to confidently deploy and manage models at scale.
1 min read
MLOps Best Practices with Azure Machine Learning
MLOps transforms ML from an experimental practice to a reliable engineering discipline. Azure ML provides the tools to implement these practices at scale.
1 min read
Understanding Feature Stores for Machine Learning
Feature stores are becoming essential infrastructure for production ML systems. Understanding these concepts will help you build more reliable and…
1 min read
Managing Data with Azure ML Datasets
Proper data management with Azure ML Datasets is foundational to building reproducible, auditable machine learning pipelines.
1 min read
Scaling Machine Learning with Azure ML Compute Clusters
Compute clusters are essential for production ML workloads. Their ability to scale dynamically and support distributed training makes them the backbone of…
2 min read
MLflow Experiments - Tracking and Managing Machine Learning Models
MLflow became the experiment tracking tool I recommend to every ML team regardless of their cloud platform choice. The core value proposition is simple…
2 min read
Building ML Workflows with Azure Machine Learning Pipelines
The first ML pipeline I helped build was a series of Python scripts duct-taped together with a bash wrapper and a nightly cron job. It worked exactly as…
2 min read
MLflow Integration with Azure Databricks for MLOps
MLflow is the closest thing to a standard the ML tooling space has right now—experiment tracking, run metadata, model registry, and a deployment abstraction…
1 min read
Azure Machine Learning Pipelines: MLOps Workflows
The first ML model I helped put into production was a notebook a data scientist ran by hand every Monday morning. That worked exactly as well as you'd…