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.
Blog
Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 75 of 92
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.
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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.
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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…
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Data Labeling Projects in Azure Machine Learning
Data labeling in Azure ML streamlines the process of creating high-quality training data, essential for building accurate machine learning models.
4 min read
Productive this Pandemic - Planning your week ahead
People have asked me lately, how do I do it? Having a full-time consulting company, spending quality time with family, doing groceries, cooking, house…
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Managing Data with Azure ML Datasets
Proper data management with Azure ML Datasets is foundational to building reproducible, auditable machine learning pipelines.
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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
Getting Started with Azure Machine Learning Compute Instances
Azure ML Compute Instances are the development environment that removes the "I can't reproduce the team's ML environment" problem for data science teams.…
1 min read
Azure Data Factory Stored Procedure Activity: Database Logic in Pipelines
The Stored Procedure Activity bridges the gap between data movement and data transformation, enabling complex database logic to be orchestrated as part of…
1 min read
Azure Data Factory Web Activity: Integrating REST APIs in Data Pipelines
The Web activity in ADF is the HTTP client that lets your pipeline call external REST APIs—triggering a Logic App, posting a Teams notification, calling a…
1 min read
Azure Data Factory Until Activity: Implementing Loops and Polling Patterns
The Until activity in ADF is the polling loop that keeps running until a condition is true—useful for pipelines that trigger an external process and need to…
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Azure Data Factory ForEach Activity: Parallel Processing Patterns
The ForEach Activity is the key to building scalable, efficient data pipelines that can process thousands of items in parallel while maintaining control and…
10 min read
The Hybrid Cloud Journey with Microsoft Azure
Hybrid cloud is the trend right now I see in a lot of companies and enterprises. The term hybrid has grown too in the last couple of years. It used to be…
1 min read
Azure Data Factory Lookup Activity: Retrieving Metadata for Dynamic Pipelines
The Lookup Activity is the foundation for building intelligent, data-driven pipelines that adapt their behavior based on configuration and runtime data.
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Dynamic Content in Azure Data Factory: Expressions and Functions
Dynamic content in ADF is the expression system that makes pipelines adaptive rather than static—generating file paths from date parameters, constructing…
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Parameterized Pipelines in Azure Data Factory: Building Reusable Data Workflows
Parameterized pipelines dramatically reduce maintenance overhead and enable rapid deployment of new data integration scenarios without code changes.
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Azure Data Factory Copy Activity: Moving Data at Scale
The Copy Activity in Azure Data Factory is the activity I configure more than any other—it's the data movement primitive that connects over 90 source and…
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Azure Data Lake Storage Gen2: Hierarchical Namespace for Big Data Analytics
Azure Data Lake Storage Gen2 provides the foundation for modern data analytics, combining the best of blob storage scalability with the directory semantics…
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Azure Blob NFS: Native NFS 3.0 Access to Blob Storage
Azure Blob NFS bridges the gap between traditional NFS workloads and cloud-native blob storage, enabling seamless migration of Linux applications without…
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Azure HPC Cache: Accelerating High-Performance Computing Workloads
Azure HPC Cache bridges the gap between cloud compute and existing storage investments, enabling organizations to run HPC workloads in Azure while…
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Azure NetApp Files: Enterprise-Grade File Storage for Demanding Workloads
Azure NetApp Files delivers enterprise storage performance in the cloud, making it the ideal choice for mission-critical applications that demand…