Locks in distributed systems give processes exclusive access to resources. Timeout and lease-based locks help prevent deadlocks and resource contention.
Apache Spark is a fast, open-source cluster computing framework for big data, supporting ML, SQL, and streaming. It’s scalable, efficient, and widely used.
Up to 70% of prompts in LLM applications are repetitive. Prefix caching can reduce inference costs by up to 90%, thus optimizing performance and saving money.
In this post, we will write a basic DNN using simple Python. To do that, we need to understand automatic differentiation and then implement it in code.
A brief introduction to Apache Cassandra for retrieval-augmented generation using Python and Ollama for developing applications free of cost locally or on a server.
Pydantic is a powerful Python library that uses type annotations to validate data structures. Learn about the powerful features of Pydantic with code examples.
February 3, 2025
by Vidyasagar (Sarath Chandra) Machupalli FBCS
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Minimize data loss and business disruption by implementing high availability and configuring disaster recovery for Loki with AWS S3 as the object store.
Learn how to build MCP servers to extend AI capabilities. Create tools that AI models can seamlessly integrate, demonstrated through an arXiv paper search implementation.
In this article, learn how to use LLMs for web scraping with ScrapeGraphAI, LangChain, and Pydantic. This guide covers setup, configuration, and data extraction
In some cases, one cannot store user-sensitive data permanently. Let's create a simple application that handles sensitive data leveraging Spring and Redis.
Explore how to easily build cloud-deployed microservices using serverless technology with Azure Functions and connect with APIs standardized in API Management.
This article discusses building an efficient ML pipeline with PySpark, covering data loading, preprocessing, model training, and evaluation for large datasets.
We'll discuss SmartXML, an XPath alternative for parsing complex XML files, converting them to SQL, and loading the results into a database seamlessly.
This article is intended for distributed systems practitioners looking to understand and implement Read Your Own Writes consistency in production environments.
Learn how to handle JSON variables in Azure DevOps pipelines, avoid escaping issues, and ensure seamless API integration with proper normalization techniques.
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