If you missed our recent blog post by Skye Scofield, Lessons from Notion: How to build a great AI product, we decided to turn it into a webinar for those of you who prefer to watch the movie instead of read the book 😎
Enjoy this on-demand viewing, and we hope you can join us next month for our Ask Me Anything virtual meetup with Ronny Kohavi!
AI has already changed the way we build software products, and changed software itself. However, incorporating AI into your product is easier said than done, especially if you’re not an AI company.
As a feature management and experimentation platform, we’ve been watching the way that AI has changed, and are noticing firsthand how the build-measure-learn framework has become more relevant in the age of AI than ever before.
In this virtual meetup, we will share how companies can successfully implement AI in their products by providing clear-cut examples and a 5-step framework for practically implementing AI in your product.
Key takeaways:
Gain a comprehensive understanding of the AI product development process.
Discover best practices for problem identification, data collection, model training, and deployment.
Acquire actionable strategies to accelerate your AI product development efforts.
Statsig is the leading experimentation and release management platform on the market but there are many SaaS platforms in this space. What's the difference between them?
Migrating from Optimizely Full Stack to Feature Experimentation enhances experiment management and feature rollout efficiency. Here's how to get started:
Tired of Optimizely? Explore Statsig's tools for simplified, efficient software feature management and robust experimentation frameworks.
Session Replay allows you to record users using your website or product, and play back those recorded sessions. Here are 5 cool ways to get started with Session Replay.
Statsig seamlessly integrates with visionOS, making it a game-changer for developers looking to make data-driven decisions.
Layers in Statsig allow variables (parameters) to be shared by many experiments. This means that once a Layer is integrated into your app's code, you can easily modify it.