All Blog Posts - Page 8

Ultimate Guide to CI/CD Best Practices to Streamline DevOps

Jesse Sumrak

How to Build a Sentiment Analysis App in Hugging Face Spaces with Interchangeable Models and AI Model Feature Flags

Start your AI journey by building a sentiment analysis app with Hugging Face Spaces and LaunchDarkly AI model feature flags. This tutorial guides you through setting up a Python environment with Streamlit, Transformers, and PyTorch, creating a Hugging Face Space, and using LaunchDarkly to switch between sentiment analysis models dynamically. By leveraging these powerful tools, you can effortlessly analyze text sentiment and experiment with different AI configurations, easily enhancing your machine-learning projects.

Erin Mikail Staples

Jun 25

How to Switch AssemblyAI Speech-to-Text Model Tiers by User Email With LaunchDarkly Feature Flags

Matt Makai

Jun 13

Introducing AI Model and AI Prompt Flags (GA)

Steve Zegalia

Experimentation

Jun 06

How to use funnel experiments in LaunchDarkly

Funnel experiments are more than just another tool in your experimentation toolbox—they’re essential for those who want to understand and optimize for the entire user journey. While A/B testing gives you a magnifying glass for single changes, funnel experiments provide relevant data across a user flow. If experimentation is a way of measuring the impact of a change made, funnel experiments calculate the best versions in a series of events or user flows. If you’re playing drop-off detective, funnel experiments can pinpoint where users vanish into the abyss. This tutorial will teach you how to set up, run, and analyze funnel experiments in LaunchDarkly.

Erin Mikail Staples

Developer productivity

May 31

DevOps vs. CI/CD: Complete Guide to Better Software Delivery

Jesse Sumrak

Developer productivity

May 28

Smoke Signals: A Comprehensive Guide to Smoke Testing in Software Development

LaunchDarkly

Experimentation

May 21

Embed powerful experiments into every feature release with LaunchDarkly

Cameron Savage

May 21

Announcing New LaunchDarkly Extensions for GitHub Copilot: AI-Powered Feature Management

Steve Zegalia

Risk mitigation

May 21

Automatically catch bugs before they're outages: meet Release Guardian

Kellye King

De-risked releases

May 21

Release Assistant: Introducing Automation, Monitoring, and UX Improvements

Steve Zegalia

Product Updates

May 21

Galaxy ‘24 Product Release

Claire Vo

Product Updates

May 16

The LaunchDarkly CLI: Stay in Developer Flow State

Karishma Irani

Product Updates

May 15

Meet the New and Improved LaunchDarkly Experience

Steve Zegalia

Feature Flags

May 03

Split Alternatives for Feature Flag Management and Experimentation

Amelie Sutsakhan

Product Updates

May 02

LaunchDarkly Joins EU-US Data Privacy Framework

Yev Feinstein

Experimentation

Apr 26

Experimentation in LaunchDarkly: feature roundup

Release meets experimentation Pairing feature management and release with experimentation is a natural fit for building exceptional user experiences. This combination allows you to understand the business impact of every release, from major features to minor bug fixes. You will no longer be rolling the dice and hoping for the best—experimentation allows you to measure, analyze, and fine-tune your product based on user data. Having feature management and experimentation built into the same tooling and processes reduces the potential technical debt or miscommunication that can occur when bouncing between tools. Let’s dive into LaunchDarkly’s experimentation feature set and see what is possible.

Erin Mikail Staples

Developer productivity

Apr 15