Building a Scalable Search Engine for Consumer Needs
Explore the creation of a scalable search engine using online machine learning systems for improved consumer experience and intent matching.
Graphs Are Everywhere - Understanding Graph Databases and Social Graphs
Explore graph databases and their applications in social networks, analytics, and decision-making. Learn about their scalability, performance, and advantages over traditional databases for interconnected data.
AI Demand Forecasting: Building a Scalable Machine Learning System for Thousands of Products
Explore ASOS's journey in building a scalable AI demand forecasting system, addressing challenges in ML modeling, data processing, and integration for improved stock management.
Simple and Constrained LLM Agents - Building Controlled AI Systems for Production
Explore building simple, constrained LLM agents for fintech, balancing control and generalizability while mitigating risks in complex production ecosystems.
Hidden in Plain Sight: Corner Case Defects - Identifying and Avoiding Unexpected Software Issues
Explore intricate, often overlooked defects in code that strike unexpectedly. Learn to identify, understand, and prevent these corner case issues through real-world examples and expert insights.
Why Should Anyone Care About Edge Computing?
Explore practical applications and benefits of edge computing with working code examples. Discover how it simplifies tasks and unlocks new possibilities in web development.
Scaling WordPress with Next.js
Leverage Next.js APIs to develop a fully static WordPress website, enhancing scalability while retaining familiar publishing experience and ecosystem support.
Tips and Tricks for Writing Fast and Maintainable Front-End Tests
Discover techniques for crafting efficient, reliable front-end tests that communicate software intent clearly. Learn to write end-to-end and component tests, prevent flaky CI runs, and make tests valuable beyond QA.
From Database in Container to DBaaS on Kubernetes
Explore building open-source DBaaS solutions on Kubernetes, addressing production challenges and user requirements for flexible, vendor-independent database deployments.
Why Postgres? - Performance, Extensions, Reliability, and More
Explore PostgreSQL's key features, including performance optimization, reliability, and extensibility, to understand its advantages for application development.
NextOps: Standardizing Software Delivery to Drive Future Innovation
Discover how to standardize software delivery without hindering innovation. Learn strategies to reduce complexity, manage tool overload, and address technical debt in CI/CD pipelines to foster creativity.
Using a Backend-as-a-Service to Easily Build Apps
Explore Appwrite, an open-source Backend-as-a-Service, to streamline app development. Learn about data storage, user authentication, and realtime functionality for faster, efficient, and scalable project creation.
System Design on Easy Mode - Durable Execution for Distributed Systems
Explore distributed systems patterns and learn how durable execution simplifies system design. Discover a new approach to solving complex architectural challenges in backend programming.
Scaling and Evolving the Machine Learning Platform at DoorDash
Explore DoorDash's journey in scaling their ML platform, focusing on prediction services, optimizations, and lessons learned for exponential growth in online ML prediction volume.
Using AI Governance to Safely Scale AI with Speed
Discover strategies for implementing AI governance to scale AI safely and efficiently. Learn to balance innovation with transparency, trust, and control in AI projects and models.
Technical Debts in Machine Learning Projects and How to Mitigate Them
Explore ML-specific risk factors in system design, understand technical debt in ML projects, and learn strategies to recognize and mitigate these challenges for improved project outcomes.
Cost Reduction Methods for Machine Learning in Production
Discover effective strategies to reduce costs when deploying machine learning models in production, focusing on optimizing models and infrastructure for cost-efficient MLOps.
Machine Learning Design Patterns - Model Monitoring and Explainable Predictions
Explore best practices in machine learning with a focus on model monitoring and explainable predictions, drawing from proven design patterns.
Simplifying AI DevOps with Model Registry
Discover strategies to streamline ML model deployment, ensure reproducibility, and enhance governance using Model Registry for efficient AI DevOps.
Fighting Pipeline Debt With Great Expectations
Learn to combat pipeline debt using Great Expectations, an open-source library for data validation, documentation, and profiling in complex data systems.
A Tale of A Thousand Pipelines: Building Scalable Deep Learning Pipelines for Time-Series Datasets
Discover scalable deep learning pipelines for massive time-series datasets, reducing costs by 80% while training hundreds of models efficiently.