Explore how LLMs enhance Python testing by automating test case generation, improving test coverage, reducing maintenance, and supporting efficient workflows.
Install CUDA on AWS GPU instances, containerize your deep learning model, and scale with ECS/EKS for cost-effective, high-performance training and inference.
AWS Step Functions Local supports mocking some services but does not support HTTP Task (http:endpoint). Instead, use the Test State API for local testing.
A monorepo-based framework that streamlines microservices testing by reducing maintenance, improving reliability, and enhancing scalability using a centralized approach.
MVI offers a structured, one-way data flow, aligning with Jetpack Compose's reactive design and ensuring clear state handling, easier debugging, and better scalability.
Build a chat history implementation with Azure Cosmos DB for NoSQL Go SDK and LangChainGo, enhancing LLM context and enabling efficient testing with Testcontainers.
Karpenter is no longer officially supported by kOps. This blog walks you through the step-by-step process of deploying Karpenter on a kOps-managed AWS Kubernetes cluster.
Testcontainers allows you to spin up lightweight, disposable containers for databases, messaging systems, and more, ensuring your tests are isolated and predictable
This article gives you a look into the crucial comparisons between Cloud and AWS, evaluating compute services, storage options, support, and costing options.
Data test engineers use automation to ensure compliance with regulations like GDPR and CCPA, safeguard sensitive data, and enhance organizational security.
This article discusses how to optimize Snowflake on AWS with advanced storage, compute, and query efficiency techniques with best practices and SQL examples.