Insights

COBOL to Containers: Running Legacy Code in a Kubernetes Environment

Modernizing legacy COBOL applications is the start for organizations aiming to enhance scalability, maintainability, and integration within modern...

Breaking Free from Mainframe Lock-in: The AI-Powered Kubernetes Approach

For decades, enterprises have depended on mainframe systems to run their critical operations. While these systems are known for their reliability and...

Visualizing Churn Predictions with Power BI and Azure ML Insights

Effective visualization of machine learning predictions is key to making data-driven decisions. Power BI, integrated with Azure Machine Learning (Azure...

Automating the Training Pipeline with Terraform and SageMaker

Objective

This article demonstrates how to automate the entire SageMaker training and deployment pipeline using Terraform and the SageMaker SDK....

Evaluating Model Performance Using SageMaker

Objective

This article explains how to evaluate the performance of a SageMaker-trained model using standard metrics for binary classification. We...

Monitoring and Debugging ML Models with Azure Application Insights

Machine learning models in production are only as effective as their reliability and performance. Monitoring, debugging, and optimizing deployed models...

Batch Inference with SageMaker Endpoints

Objective

This article demonstrates how to perform batch inference using SageMaker endpoints, focusing on handling large...

Automating Customer Churn Data Pipelines with Azure Storage and ML Studio

For machine learning workflows to operate effectively, seamless data ingestion and preprocessing are essential. Azure Storage and Azure Machine Learning...

Deploying and Configuring a Real-Time Endpoint

Objective

This article explains how to deploy a trained XGBoost model to a SageMaker endpoint for real-time inference. We will cover deployment...