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Roles of Feature Management & Experimentation in AI Development

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Roles of Feature Management and Experimentation in AI Development

The following article is a recap of a Flagship 2023 talk given by Brian Bell, CEO of Split. You can watch the full on-demand video here

Welcome to the New Era of AI Software Development

From image generators that can spin up detailed drawings in a moment’s notice to chatbots that can fully form articles at the command of a few prompts, AI is quickly revolutionizing the software landscape. 

Companies can now build AI apps at an unprecedented pace. The technology that was once limited to an elite few is now accessible to every engineer. All you need to do is begin with a pre-trained model and start building. 

It’s no surprise that so many Split customers are quickly implementing AI into their applications. And, while AI development continues to move our world forward, it’s important to recognize the critical role that feature management and experimentation plays in promoting fast, safe, and effective AI releases.

“Driving the future of feature management and experimentation means recognizing how it plays a role in safely delivering new AI models across the world of agile software development.”

Brian Bell, Split CEO

The Challenges of Releasing AI 

The new era of AI development has its own laundry list of challenges for product development teams. How do we build and release AI models quickly enough to keep pace with the industry? How do we do it safely and without disrupting the user experience? How do we fine-tune our chatbots for optimal performance and maxized results? 

According to Brian Bell, CEO at Split, “You can’t do AI right without experimentation.” He believes that the companies who will inevitably lead the industry in AI software development will be those who embrace experimentation capabilities to the fullest. “They’ll continuously test different models and parameters; they’ll launch faster than anyone; they’ll gather valuable data, so they can innovate solutions that matter,” says Brian Bell. 

“You can’t do AI right without experimentation.”

Brian Bell, Split CEO

5 Steps to AI Development Success

According to Brian Bell, following these five critical steps can optimize your AI development, ensuring it’s safe, effective, and quick to market.

  1. Build AI Features 
  2. Release with different prompts, parameters, and models
  3. Measure against guardrail metrics
  4. Analyze the impact
  5. Apply better performing models 

To power each step of the process, it takes an intelligent feature management and experimentation platform like Split. One that automatically monitors every AI solution you rollout. One that instantly sends alerts and actions, helping you triage and iterate AI on the fly. An enterprise-grade platform that finally puts the A/B testing in the hands of the AI developer.

Watch Brian Bell’s Full Flagship Presentation on AI

Curious about experimentation’s role in AI software development? Don’t miss Brian Bell’s full-length presentation at Flagship 2023. This on-demand video and more is available at split.io/flagship-2023, where you can learn from the leaders in feature management and experimentation and get the Building Blocks of Best Practice.

Switch It On With Split

The Split Feature Data Platform™ gives you the confidence to move fast without breaking things. Set up feature flags and safely deploy to production, controlling who sees which features and when. Connect every flag to contextual data, so you can know if your features are making things better or worse and act without hesitation. Effortlessly conduct feature experiments like A/B tests without slowing down. Whether you’re looking to increase your releases, to decrease your MTTR, or to ignite your dev team without burning them out–Split is both a feature management platform and partnership to revolutionize the way the work gets done. Schedule a demo to learn more.

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