Introduction: Google Cloud’s operations suite (formerly Stackdriver) provides comprehensive observability through Cloud Logging, Cloud Monitoring, Cloud Trace, and Error Reporting. This guide explores enterprise observability patterns, from log aggregation and custom metrics to distributed tracing and intelligent alerting. After implementing observability platforms for organizations running thousands of microservices, I’ve found GCP’s integrated approach delivers exceptional […]
Read more →Month: March 2025
Azure Machine Learning: A Solutions Architect’s Guide to Enterprise MLOps
The journey from experimental machine learning models to production-ready AI systems represents one of the most challenging transitions in modern software engineering. Having spent over two decades architecting enterprise solutions, I’ve witnessed the evolution from manual model deployment to sophisticated MLOps platforms. Azure Machine Learning stands at the forefront of this transformation, offering a comprehensive […]
Read more →Difference between workload managed identity, Pod Managed Identity and AKS Managed Identity
Azure Kubernetes Service(AKS) offers several options for managing identities within Kubernetes clusters, including AKS Managed Identity, Pod Managed Identity, and Workload Managed Identity. Here’s a comparison of these three options: Key Features AKS Managed Identity Pod Managed Identity Workload Managed Identity Overview A built-in feature of AKS that allows you to assign an Azure AD […]
Read more →Structured Output from LLMs: Instructor Library and Production Patterns (Part 2 of 2)
Introduction: Getting LLMs to return structured data instead of free-form text is essential for building reliable applications. Whether you need JSON for API responses, typed objects for downstream processing, or specific formats for data extraction, structured output techniques ensure consistency and parseability. This guide covers the major approaches: JSON mode, function calling, the Instructor library, […]
Read more →LLM Deployment Strategies: From Model Optimization to Production Scaling
Introduction: Deploying LLMs to production is fundamentally different from deploying traditional ML models. The models are massive, inference is computationally expensive, and latency requirements are stringent. This guide covers the strategies that make LLM deployment practical: model optimization techniques like quantization and pruning, inference serving with batching and caching, containerization with GPU support, auto-scaling based […]
Read more →Python Machine Learning Frameworks: Scikit-learn, TensorFlow, and PyTorch Compared
Compare Python’s leading ML frameworks for enterprise deployments. Learn when to use Scikit-learn for classical ML, TensorFlow for production deep learning, and PyTorch for research flexibility with production-ready code examples.
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